Potential Value of PRKDC as a Therapeutic Target and Prognostic Biomarker in Pan-Cancer

doi:10.21203/rs.3.rs-789778/v1

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Potential Value of PRKDC as a Therapeutic Target and Prognostic Biomarker in Pan-Cancer

https://doi.org/10.21203/rs.3.rs-789778/v1

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Background

While PRKDC (Protein Kinase, DNA-Activated, Catalytic Subunit) plays an important role in double-strand break (DSB) repair to retain genomic stability, there is still no pan-cancer analysis based on large clinical information on the relationship between PRKDC and different tumors. For the first time, this research used numerous databases to perform a pan-cancer review for PRKDC to explore the possible mechanism of PRKDC in the etiology and outcomes in various tumors.

Methods

PRKDC's expression profile and prognostic significance in pan-cancer were investigated based on various databases and online platforms, including TIMER2, GEPIA2, cBioPortal, CPTAC, and SangerBox. We applied the TIMER to identified the interlink of PRKDC and the immune infiltration in assorted tumors, and the SangerBox online platform was adopted to find out the relevance between PRKDC and immune checkpoint genes, TMB, and MSI in tumors. GeneMANIA tool was employed to create a Protein-Protein Interaction (PPI) analysis, gene set enrichment analysis (GSEA) was conducted to performed gene enrichment analysis.

Results

Overall, tumor tissue presented a higher degree of PRKDC expression than adjacent normal tissue. Meanwhile, patients with high PRKDC expression have a worse prognosis. PRKDC mutations were present in almost all TCGA tumors and might lead to a better survival prognosis. PRKDC expression level was shown a positive correlation with tumor-infiltrating immune cells (TIICs). PRKDC high expression cohorts were enriched in "cell cycle," "oocyte meiosis," and "RNA-degradation" signaling pathways.

Conclusions

This study revealed the potential value of PRKDC tumor immunology and as a therapeutic target and prognostic biomarker in pan-cancer.

Biomedical Engineering

Biomaterials

prkdc

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Cells encounter many DNA lesions every day that imperil their genomic completeness, and the most toxic one is DSB. As we all know, one unrepaired DSB might induce cell death, rather a misrepaired DSB can lead to chromosomal abnormalities (e.g., deletions, translocations, and fusions), which may contribute to a loss of heterozygosity, induce genetic instability, neoplastic transformation, and ultimately cancer.

Organisms have developed wrought genomic stability maintenance systems to prevent cancer (1) by identifying broken DNA sites and restoring the DNA damage (2). In human cells, DSB repair is mediated by two distinguished ways: non-homologous final junction (NHFJ) and homologous recombination (HR), and NHEJ was considered quicker and more efficient than HR (3).

DNA-dependent protein kinase catalytic subunit (DNA-PKcs) or serine/threonine protein kinase catalytic subunit are the other names for PRKDC. It belongs to the phosphatidylinositol 3-kinase-like (PIKK) family and presents at approximately all mammalian cells (4). PRKDC interacts with Ku70/Ku80 heterodimer, then together join into the ligation step of the NHEJ process, ultimately helps to preserve genomic integrity (5).

PRKDC has recently attracted a lot of interest in being a therapeutic target and promising biomarker for many human cancers (6–13), but a pan-cancer investigation of the correlation between PRKDC with various types of tumors based on large-scale clinical data is currently lacking. In this study, for the first time, we conducted a pan-cancer analysis of PRKDC based on a variety of databases and online platforms to investigate the latent molecular processes of PRKDC in different tumor’s pathogenesis and clinical outcomes.

3.1 PRKDC expression analysis results

We adopted the TCGA and GTEx datasets to look at PRKDC expression levels across diverse tumors. The expression of PRKDC in the tumor tissues of CESC, LGG, LIHC, LUAD, LUSC, BLCA, BRCA, CHOL, DLBC, READ, STAD, THYM, UCS, COAD, ESCA, and HNSC were higher compare with those in neighboring normal tissues, as illustrated in Figs. 1A and 1B. By contrast, PRKDC expression levels were significantly lower in tumor tissues of KIRC, KIRP, and THCA. But there was no significant difference in other tumors, including GBM, KICH, PAAD, PCPG, PRAD, UCEC, ACC, LAML, OV, SARC, SKCM, and TGCT (all P>0.05), as indicated in Fig. 1A and Fig. S1A.

We applied the “Pathological Stage Plot” modular of GEPIA 2 to confirmed a positive association between PRKDC expression and advanced cancer stage in OV, SKCM, and UCEC (all P<0.05, Figure 1C) but not the others (Fig. S1B). The results from the CPTAC dataset confirmed that PRKDC total protein is highly expressed in breast cancer, ovarian cancer, colon cancer, UCEC, and LUAD compared with normal controls, but not clear cell RCC (Fig. 1D).

3.2 Survival analysis results

We explored the prognosis value of PRKDC in pan-cancer base on several databases and platform. Firstly, we evaluated the association between PRKDC and prognosis (OS and DFS) using GEPIA 2. As shown in Fig. 2A, it suggested a poorer OS in high PRKDC expression cohort rather than low PRKDC expression cohort of LIHC (HR =1.6, P =0.01), ACC (HR =2.8, P =0.015), LGG (HR =1.9, P =0.00099), LUAD (HR =1.5, P =0.0045), MESO (HR =2.2, P =0.002), SARC (HR =1.8, P =0.0042) and UVM (HR =3.2, P =0.023). High PRKDC expression cohorts also exhibited shorter DFS (disease-free survival) than low PRKDC expression cohorts for ACC (HR =2.6, P =0.0069), KIRP (HR =2, P =0.017), LGG (HR =1.5, P =0.018), MESO (HR =2, P =0.016), and SARC (HR =1.4, P =0.048).

Next, survival data from the SangerBox platform revealed a link between high PRKDC expression and bad OS (Fig. 2B) for LUAD (P =0.015), UCEC (P =0.0037), BLCA (P =0.015), PAAD (P =0.019), KIRP (P =0.0067), LIHC (P =0.00038), SARC (P =0.0084), MESO (P =0.0031), LGG (P =0.000047), KICH (P =0.0023), ACC (P =0.007) and UVM (P =0.0015), but a greater OS in KIRC (P =0.049) and READ (P =0.013). High PRKDC expression cohort was related to a shorter DFI (disease-free interval) (Fig. 2C) in PAAD (P =0.003) and KIRP (P =0.013) when compared with low PRKDC expression cohort. Similar to OS, high PRKDC expression group showed worse DSS (disease-specific survival) (Fig. 2D) compare with low PRKDC expression group for LUAD (P =0.013), UCEC (P =0.0071), BLCA (P =0.0048), PAAD (P =0.017), KIRP (P =0.0024), LIHC (P =0.0085), SARC (P =0.0099), MESO (P =0.002), LGG (P =0.000081), KICH (P =0.00077), ACC (P =0.0088) and UVM (P =0.00096). Additionally, high PRKDC expression was relevant with shorter PFI (progression-free interval) for LUAD (P =0.007), UCEC (P =0.0017), PAAD (P =0.012), KIRP (P =0.0094), LIHC (P =0.027), MESO (P =0.0078), LGG (P =0.0027), KICH (P =0.012), ACC (P =0.000076) and UVM (P =0.000083).

Finally, we evaluated prognostic value of PRKDC in pan-cancer (OS and RFS) on Kaplan-Meier plotter database (https://kmplot.com/analysis/). PRKDC level negatively correlated with the OS (Figure S2) in following cancers: BLCA (P =0.03), BRCA (P =0.025), CESC (P =0.003), KIRP (P =0.0095), LIHC (P =0.0067), LUAD (P =0.001), PAAD (P =0.0069), SARC (P =0.00015), THCA (P =0.0069) and UCEC (P =0.0001), but was positively correlated with the OS in ESCA (P =0.0058), KIRC (P =0.0012) and THYM (P =0.021). There was a negative relation between PRKDC level and RFS (relapse-free survival) (Figure S3) in KIRP (P =0.013), LIHC (P =0.029), PAAD (P =0.00038), THCA (P =0.015), UCEC (P =0.0061), and SARC (P =0.011), but a positive correlation was shown in ESCA (P =0.0075) and OV (P =0.011).

The above data revealed that PRKDC is elevated in most human tumors, suggesting that it may serve as a biomarker for poor prognosis, though varied across different types of tumors.

3.3 PRKDC alteration analysis results

Because genomic mutations are linked to tumor oncogenesis and progression, we observed 10953 patients from 32 TCGA studies in total (https://www.cbioportal.org), and found that 804 (7%) of them carried at least one PRKDC gene mutation. As it is indicated in Fig. 3A, “mutation” showed the highest frequency of PRKDC alteration in most tumors, including UCEC, SKCM, STAD, LIHC, LUAD, COAD, BLCA, HNSC, ESCA, LUSC, CESC, SARC, MESO, KIRP, KIRC, ACC, GBM, LGG, THYM, and THCA. In particular, “mutation” was the only genetic alteration type in KIRP (~2.5% frequency), ACC (~2% frequency), THYM (~1% frequency), and THCA (~1% frequency). PRKDC mutation frequency was the highest in UCEC (>18%), and the “amplification” alteration type was the primary type in UCS, with an alteration frequency nearby 15%.

The alteration types, mutated sites, and mutation cases numbers were exhibited in a schematic representation of PRKDC protein structure in Fig. 3B. It showed that “Truncating” is the most common sort of genetic alteration, and N3604Kfs*3/Tfs*48 alteration was detected in 6 cases of UCEC and 6 cases of STAD.

Additionally, we compared the survival prognosis in cohorts with or without PRKDC genetic alteration across different tumors. It indicated a better prognosis for PFS (P =1.862e-03), DSS (P =4.566e-03), and OS (P =5.864e-03) in UCEC cases (Fig. 3C) with altered PRKDC, and better prognosis for PFS (P =1.582e-03) and DSS (P =0.0366) in STAD cases (Fig. 3D) with altered PRKDC, compared with cases without PRKDC alteration.

3.4 Protein phosphorylation analysis results

Using the CPTAC dataset, we evaluated the PRKDC phosphorylation level in tumor tissues as well normal tissues in six kinds of cancers, including breast cancer, colon cancer, clear cell RCC, UCEC, LUAD, and ovarian cancer. A schematic diagram summarizes the PRKDC phosphorylation sites and related tumors (Fig. 4A). The S893 locus of PRKDC exhibited an increased phosphorylation levels in colon cancer (Fig. 4C, P =2.5e-04), LUAD (Fig. 4D, P =5.16e-11), ovarian cancer (Fig. 4E, P =1.12e-05) and UCEC (Fig. 4F, P =3.49 e-03), but a decreased phosphorylation level in clear cell RCC (Fig. 4G, P =1.08e-18). Additionally, we confirmed that PRKDC phosphorylation of S893 was experimentally proved by one research (14) through the phosphoNET database (http://www.phosphonet.ca/). To learn more about the significance of S893 phosphorylation in carcinogenesis, further molecular investigations are needed.

3.5 Immune infiltration analysis results

TIICs from tumor microenvironment (TME) are correlated with initiation, progression or metastasis of tumor (15), so we explored the coefficient of TIICs and PRKDC expression in diverse tumors of TCGA by using TIMER database. According to the findings, PRKDC was relevant to tumor purity in nine different kinds of cancers, and also remarkably associated with the infiltration levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells in 15, 21, 14, 18, 21, and 19 kinds of tumors, separately (Figure 5A and Figure S4-7). In addition, KIRC, LIHC, and THCA show the most strongly association between PRKDC and immune infiltrating levels (Figure 5B). For KIRC, a negative interrelation was confirmed between PRKDC gene and tumor purity (r =−0.105, P =2.41e-2), but PRKDC expression and B cells (r =0.295, P =1.15e-10), CD8+ T cells (r =0.165, P =5.26e-04), CD4+ T cells (r =0.341, P =5.47e-14), macrophages (r =0.468, P =8.12e-26), neutrophils (r =0.468, P =2.56e-26) and dendritic cells (r =0.408, P =1.16e-19) indicated a positive association. In LIHC, PRKDC expression was positively correlated with B cells (r =0.324, P =7.12e-10), CD8+ T cells (r =0.205, P =1.33e-04), CD4+ T cells (r =0.358, P =7.42e-12), macrophages (r =0.405, P =6.99e-15), neutrophils (r =0.422, P =2.38e-16) and dendritic cells (r =0.42, P =5.73e-16), but was not associated with tumor purity. About THCA, PRKDC gene shown a negative association with CD8+ T cells (r =−0.437, P =3.61e-24), but there was a positive association with B cells (r =0.672, P =1.20e-64), CD4+ T cells (r =0.665, P =1.01e-63), macrophages (r =0.715, P =1.09e-77), neutrophils (r =0.428, P =3.65e-23), and dendritic cells (r =0.386, P =1.10e-18), but was not related to tumor purity. We employed the SangerBox online tool to discuss the relevance between PRKDC and immune cell subtypes in the TME (Figure 5B). The results indicated that CD56dim natural killer cell, memory B cell, monocyte and type 2 T helper cell had the strongest correlation with PRKDC expression.

3.6 Immune checkpoint genes, TMB and MSI analysis results

Immune checkpoints are important immune system regulator. Some tumors can protect themselves from attack by taking advantage of immune checkpoint genes. We used SangerBox platform to investigate the association between PRKDC and the immune checkpoint genes across different tumors, as shown in figure 6A. For example, in LIHC, PRKDC was positively correlated with expression of CD200, CD200R1, CD244, CD27, BTNL2, CD274, CD276, CD44, HAVCR4, CD28, CD80, CD86, CTLA4, HHLA2, ICOSLG, LGALS9, NRP1, TNFSF15, ICOS, LAG3, LAIR1, PDCD1, TNFSF4, TNFSF9, VSIR, VTCN1, TIGIT, TNFRSF8, and TNFSF18. TMB and MSI are important factors that influence tumor initiation and development, as well as tumor immunotherapy response. Higher TMB was related to better OS and better outcomes from ICI therapy in tumor patients(16). Radar plots (Fig. 6B) showed a positive association between PRKDC and TMB in KICH (P =0.0027) and LUAD (P =1.1e-05), but a negative association in THCA (P =0.008) and COAD (P =0.021). We also observed a positive association between PRKDC expression and MSI for GBM (P =0.033), OV (P =0.0018), LUSC (P =0.0013), SARC (P =0.007) and KIRC (P =0.0048) but a negative correlation for PRAD (P =1.1e-05), SKCM (P =0.009), THCA (P =0.00015), HNSC (P =0.0059), and DLBC (P =0.00044) (Fig. 6C). In summary, all these data suggested a broad association between high PRKDC expression and tumor immunity.

3.7 PPI network and enrichment analysis results

In order to investigated the mechanism of PRKDC in tumorigenesis, this work employed GeneMANIA online platform to build a PPI network for PRKDC. As it is shown in Figure 7A, PRKDC shared a same pathway with XRCC4, XRCC5, XRCC6, and LIG4, and PRKDC had strong physical interactions with XRCC5 and XRCC6. The aforementioned two genes encode Ku80 and Ku70 respectively, which make up a heterodimer protein critical for NHEJ pathway of DNA repair. As a regulatory subunit, Ku80/Ku70 heterodimer can induce a 100-fold increase in affinity between DNA-PKcs and DNA. LIG4 is also required for NHEJ because it can form a complex with XRCC4 that further interacts with the DNA-PK (17).

Enrichment analysis of high PRKDC expression and low PRKDC expression was performed using GSEA (Figure 7B-E). The top few KEGG enrichment terms in high PRKDC expression group named “cell cycle”, “oocyte meiosis” and “RNA-degradation”. HALLMARK revealed enrichment terms of high PRKDC expression in “mitotic spindle”, “G2M check point” and “P13K-AKT-MTOR signaling”.

Functional associations between PRKDC and various tumors have been reported in many publications (10, 18–22). It is unclear that whether PRKDC is involved in the etiology of various cancers via common molecular processes. There was no publication with PRKDC pan-cancer analysis from a holistic oncology approach, according to a literature search. Therefore, we conducted a comprehensive study of PRKDC genes in 33 types of cancers based on TCGA, GTEx and CPTAC databases, including gene expression, gene alteration, protein phosphorylation, tumor microenvironment and biological pathways.

Inducing irreversible DNA damage is considered to be a significant therapeutic strategy for cancer. DNA-dependent protein kinase complex (DNA-PK) consisted of DNA-PKcs and Ku80/ Ku70 heterodimer protein, and it’s very important to DSB repair response. Thus, it explains why DNA-PK is associated with a reduced response to DNA-damaging drugs as well as treatment resistance in various malignancies (7, 23, 24). This study confirmed that PRKDC was substantially expressed in most tumors and was linked to a poor prognosis in a lot of tumor types (25). The development of PRKDC targeted therapy has been prompted by these evidences and the fact that DNA-PKcs is a latent treatment target (26, 27). There are many pathways that can promote tumor cells survival and proliferation, so knowing PRKDC’s regulatory mechanisms is crucial for designing effective therapies to inhibit PRKDC.

Only 10 cases have been recorded with disease-causing mutations in PRKDC. Six of them exhibited severe combined immunodeficiency, including granulomas and autoimmunity (28–30). It results from that PRKDC mutation decreases affinity between DNA-PKcs and DNA and jeopardizes the activity of Artemis, which is necessary for V(D)J recombination as well. We found that over 18% of UCEC patients and about 12% of STAD patients presented alteration in PRKDC and the alteration correlates with better survival. In a recent study (31), PRKDC mutation was found to be related to higher TMB, elevated mRNA levels of immunity-related genes as well as improved response to ICI (immune checkpoint inhibitor) treatments in tumor individual. Another publication also discovered that PRKDC mutation was linked to an advanced TMB and MSI states in tumors, and knocked down PRKDC or used a DNA-PKcs inhibitor might improve ICI efficacy (32). Above findings demonstrate that the PRKDC mutation might be used as a biomarker in immunotherapy.

We used the CPTAC dataset to compared the DNA-PKcs phosphorylation levels in the following six types of tumors: LUAD, colon cancer, clear cell RCC, breast cancer, UCEC, and ovarian cancer. It indicated a higher phosphorylation level at the S893, T2609, S2671, S3205, and S3995 locus in the primary tumors than normal controls. In vitro, DNA-PKcs experiences substantial autophosphorylation, causing it to dissociate from Ku-bound DNA and lose its kinase function (33). In contrast, the capacity of DNA-PKcs to disassociate from Ku-DNA was diminished when the T2609 phosphorylation site was changed to alanine (34, 35). As a result, DNA-PKcs autophosphorylation was considered critical for the DNA repair process.

PRKDC expression has been linked to various levels of immune infiltration in tumors, particularly in KIRC, LIHC, and THCA, according to this study. There was a strong negative relevance between PRKDC and tumor purity in KIRC, showing that PRKDC is relatively abundant in the tumor microenvironment. However, the degree of PRKDC expression in LIHC and THCA was independent of tumor purity, indicating that it was expressed equally among tumor cell and the tumor microenvironment. For all three tumors, it showed a clearly positive associations between PRKDC expression and TIICs, but a negative association with CD8 + T cells in THCA. These distinctions indicated that there were variances among tumors in TME.

In the tumor microenvironment, immune system could identify and kill tumor cells. However, tumor cells can use a variety of tactics to survival and proliferate that inhibit the immune system (36, 37). Tumor immunotherapy, which includes of therapeutic antibodies, immune checkpoint inhibitors, tumor vaccines, and cell treatments, can help the body regain its natural antitumor immune response. We looked examined the link between PRKDC expression and 47 different immunological checkpoint genes, and found that high PRKDC expression may play a key role in immune evasion.

MSI was considered a biomarker for identifying individuals who may benefit from immunotherapies since it is linked to a higher cancer risk with important clinicopathological features, such as elevated TMB and more TIICs (38). TMB might be adopted as a biomarker to forecast how well checkpoint blockades may work (39, 40). We showed evidence of a link between PRKDC expression and MSI or TMB across pan-cancers in this work, as well as the potential function of PRKDC in oncology immunological and as a predictive biomarker for a variety of malignancies.

In conclusion, our study revealed statistical associations between PRKDC expression and clinical outcome, genetic mutation, protein phosphorylation, immune cells infiltration, immune checkpoints, TMB, and MSI across pan-cancers, and confirmed PRKDC's latent involvement in tumor immunology and as a therapeutic target and prognostic biomarker. Unfortunately, this work merely performed a bioinformatics analysis of PRKDC across several databases and platforms, lacking of in vitro or in vivo experiments. So further cellular and molecular mechanistic research on PRKDC is desired to better understand the role of PRKDC in tumors.

2.1 PRKDC expression analysis

By using TIMER2.0 (Tumor Immune Estimation Resource, version 2, http://timer.cistrome.org/ ) database (41), the expression levels of PRKDC in tumor tissues compared with neighboring normal tissue were analyzed in all TCGA (The Cancer Genome Atlas, https://www.cancer.gov/ ) tumors. For those tumors without normal tissues, we used the GEPIA 2 (Gene Expression Profiling Interactive Analysis, version 2, http://gepia2.cancer-pku.cn) (42) to generate boxplots from the GTEx (Genotype-Tissue Expression, https://gtexportal.org/home/ ) database, and selected “Match TCGA normal and GTEx data” pattern. This study furthermore generated violin plots of PRKDC expression according to pathological stages of all TCGA tumors by using the “Pathological Stage Plot” modular in GEPIA 2, with cutoff values set to P value =0.01 and log2 FC (fold change) =1. TPM (Transcripts per million) values were transformed into a log2 (TPM+1).

The UALCAN (http://ualcan.path.uab.edu/ )(43) enabled us to carried out phosphorylation investigations of PRKDC protein across various tumors from the CPTAC (Clinical proteomic tumor analysis consortium) database(44). This work compared PRKDC total protein and phosphoprotein expression levels in six tumors and neighbor normal tissues, including breast cancer, ovarian cancer, colon cancer, clear cell RCC (Renal cell carcinoma), UCEC (Uterine corpus endometrial carcinoma), and LUAD (Lung adenocarcinoma).

2.2 Survival analysis

Connection between PRKDC expression and survival of all TCGA cancers was investigated using "Survival Map" and "Survival Analysis" modules of GEPIA 2. In order to separate the high and low PRKDC expression groups, a 50 percent cutoff value was chosen. SangerBox online website (http://sangerbox.com ) is a comprehensive Chinese bioinformatics analysis platform. The “Gene-KM plotter” module of SangerBox was used to compare the OS (overall survival), DFI (disease-free interval), DSS (disease-specific survival), and PFI (progression-free interval) in high and low expression cohorts. HR (hazard ratios) were computed using a 95% confidence interval and a P value.

2.3 Genetic alteration analysis

We acquired PRKDC mutation status in tumor patients from the cBioPortal (http://www.cbioportal.org/) platform (45). The “Cancer Types Summary” section displayed the frequency of alteration, types of mutation, and CNA (Copy number alteration) data. The “Mutations” module presented the mutation site information in the PRKDC protein structural schematic diagram. Using the “Comparison/Survival” module, Kaplan-Meier plots were created to compare DSS, OS, DFS (disease-free survival), and PFS (progression-free survival) in cohorts with or without PRKDC genetic mutation, with log-rank P values.

2.4 Immunological analysis

In order to observe the interaction between tumor cells and immune cells, the TIMER (Tumor Immune Estimation Resource, https://cistrome.shinyapps.io/timer/ ) database (46) was employed. By using “Gene” module of TIMER, we determined the tumors’ purity and discovered the relationship between PRKDC and the quantity of six TIICs subsets in 39 kinds of tumors with Spearman correlations. CD4+ T cells, CD8+ T cells, B cells, neutrophils, dendritic cells (DC), and macrophages are among these six categories of TIICs. The correlations between PRKDC and 28 subtypes of immune cell were determined and evaluated via the “Gene-Immune Analysis” module of SangerBox. We also looked at the relational between PRKDC and 47 kinds of gene markers of TIICs in distinct tumors (47).

The all somatic nonsynonymous mutation counts per megabase in coding sequence were used to calculate the tumor mutation burden (TMB), which may be a promising marker for predicting immunotherapy response (48). MSI (microsatellite instability) is a molecular tumor characteristic characterized by spontaneous nucleotide loss or gain on short tandem repetitive DNA sequences, and is the result of a defective DNA mismatch repair (MMR) system (49). Analysis and visualization of TMB and MSI in different tumors were carried out using SangerBox online platform.

2.5 PPI network and enrichment analysis

We built a PPI network for PRKDC using the GeneMANIA online platform (https://genemania.org/ ) (50) to find out the mechanism of PRKDC in tumorigenesis. The first five terms of KEGG (Kyoto encyclopedia of genes and genomes) and HALLMARK analyses were displayed in the high/low PRKDC expression cohorts, respectively, to discover the biological signaling pathway. |NES| > 1, P< 0.01, and FDR < 0.25 were defined as significant gene sets (51).

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

The raw data of this study are derived from the GTEx (https://gtexportal.org/home/ ) database and the TCGA ( https://www.cancer.gov/ ) database, which are publicly available databases.

Disclosure of interest

The authors report no conflict of interest.

Funding

National Natural Science Foundation of China (81670596).

Author Contributions

XS and XY designed this work. FY performed the bioinformatics analyses. All authors participated in writing the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors sincerely acknowledge the publicly available of the GTEx (https://gtexportal.org/home/ ) database and TCGA ( https://www.cancer.gov/ ) database.

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Potential Value of PRKDC as a Therapeutic Target and Prognostic Biomarker in Pan-Cancer

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