Pan-cancer study of PARK7/DJ-1 as a biomarker for prognostic and immunological role in 33 human cancers

doi:10.21203/rs.3.rs-1450125/v1

Download PDF

Research Article

Pan-cancer study of PARK7/DJ-1 as a biomarker for prognostic and immunological role in 33 human cancers

https://doi.org/10.21203/rs.3.rs-1450125/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: An increasing number of studies have suggested that PARK7 expressed at a high level in various tumors and is closely associated with malignant tumor generation, progression and prognosis. However, there are relatively few studies on the specific role of PARK7 in tumor immunity.

Methods: Gene expression data and clinical characteristics for 33 cancers were obtained from The Cancer Genome Atlas database. The correlations between PARK7 with clinical Indicators and survival were analyzed. To investigate the role of PARK7 in cancer immunotherapy, we analyzed the relevance of PARK7 to the tumor microenvironment and its relationship with immune cell infiltration, immune related genes. In addition, we investigated the correlation between PARK7 and immunotherapeutic biomarkers and explored the underlying pathways of PARK7.

Results: Higher expression levels were seen in tumor samples of BLCA, BRCA, COAD, ESCA, GBM, HNSC, LIHC, LUAD, LUSC, PRAD, STAD, UCEC than normal tissues. Regarding the prognostic value of PARK7, the results of the multivariate COX regression model analyses revealed PARK7 as an independent prognostic factor in KICH, KIRP, LAML and UVM. PARK7 were significantly correlated with immune cell infiltration, MSI, TMB and some immune related genes in various cancers. Patients with low PARK7 expression in the GSE67501 cohort were more responsive to immunotherapy (p = 0.024).

Conclusion: This study suggested that PARK7 can be used not only as a prognostic biomarker for some cancers, but also as a potential biomarker for assessing the response to immunotherapy. These findings can provide some theoretical basis for future research, while more research is required to prove these findings.

PARK7

pan-cancer

prognosis

immunotherapy

biomarker

Parkinson disease (PARK) genes family are genes related to the occurrence and development of Parkinson's disease¹. The PARK7 gene maps to 1p36.12-13 in the human genome, with a total length of 24 ku and a molecular weight of 20 kDa². It encodes the PARK7 protein, which is also kown as DJ-1 / RS / CAP1 protein³. This protein is highly conserved and is ubiquitously expressed in all tissues and organs, including the brain^{4, 5}.

DJ-1 was initially suggested to be a novel oncogene protein owing to its high cooperative transformation activity with H-Ras or c-Myc⁶. PARK7 has been shed more light on the field of Parkinson's disease⁷. Several mutations in PARK7 are major associated with autosomal inherited early-onset Parkinson’s disease⁸. As the research intensified, many functions of PARK7 protein were discovered, including: transcriptional regulation, antioxidative stress, protein chaperone, transcription function and mitochondrial regulation^{9, 10}. Unfortunately, none are fully understood. There are accumulating studies revealed that PARK7 is expressed at high levels in many tumor types, and Its overexpression is closely related to the generation, progression and prognosis of malignant tumors^{2, 11–15}. Of note, silencing PARK7 was reported to increase the sensitivity of tumor cells to cisplatin and dihydroartemisinin^{16, 17}. These findings indicate that PARK7 can be used not only as a prognostic biomarker, but also as an anti-tumor therapeutic target.

PARK7 has a non-negligible role in immune modulation, mainly for regulatory T cells (Tregs)^18–20. Tregs are immune cells with negative regulatory significance, which are critical for maintaining effective immune tolerance and the homeostasis of various immune cells^{21, 22}. However, Tregs in the tumour microenvironment can strongly dampen the immune activity of anti-tumour T cells, help tumor cells escape immune surveillance, thus providing conditions for tumor progression^{23, 24}. PARK7 participates in the differential regulation of natural Tregs and induced Tregs. What’s more, Tregs cell lacking PARK7 are defective in replication, proliferation, and more prone to cell death. Generally, low PARK leads to a decrease in the number of total Tregs¹⁹.

However, there are relatively few studies on the specific role of PARK7 in tumor immunity. This study investigated the expression of PARK7 in 33 tumors, its influence on the immune microenvironment, and the relationship with existing immune markers. The purpose of this study is to further elucidate the role of PARK7 in tumor immunity and provides evidence for serving as an immunotheraputic target.

Data collection

We acquired the clinicopathological and genomic information for 33 types of cancer from the University of California Santa Cruz Xena Explorer (cohort: TCGA Pan-Cancer) and The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). Next, we obtained somatic mutation data from the TCGA database. Then, We performed a systematic search to identify the immune checkpoint blockade therapeutic cohorts, which reported with complete clinical information and could be publicly retrieved. Finally, we employed three immunotherapeutic cohorts in this study: metastatic melanoma with pembrolizumab treatment (GSE78220 cohort downloaded from the Gene expression omnibus database, GEO), and renal cell carcinoma with nivolumab treatment (GSE67501 cohort downloaded from GEO); advanced urothelial cancer with atezolizumab intervention (IMvigor210 cohort downloaded from previously published research)²⁵.

Clinical correlation between PARK7 expression and various cancers

We performed differential gene expression analysis to determine whether PARK7 expression differed between cancer and paraneoplastic tissues and how PARK7 was differentially expressed in 33 cancers by the limma package in R studio software. We also investigated the correlation between PARK7 expression and other clinical parameters (age, gender, and tumor stage). In addition, by the survival package in R, we constructed survival curves using the Kaplan-Meier method and compared the differences between the PARK7 high and low expression groups using the log-rank test and performed multivariate Cox regression analyses to investigate the time-dependent prognostic value of PARK7 in 33 cancers: overall survival (OS; time from treatment initiation to death from any cause), disease-free survival (DFS; the period from treatment initiation to disease recurrence or death from any cause), disease-specific survival ( DSS; cancer survival in the absence of other causes of death) and progression-free survival (PFS; the period from treatment initiation to disease progression or death from any cause). In the univariate analysis, we retained the result of p < 0.05. In the multifactorial Cox regression analysis, positivity indicated that PARK7 was an independent prognostic factor for this tumor, and a risk ratio above 1 (HR > 1) indicated that the exposure factor (PARK7 expression) was a promoter of the positive event (death), and HR < 1 indicated that it was a suppressor. * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001.

Generation and investigation of PARK7 activity

We calculated PARK7 gene activity using single-sample gene set enrichment analysis (GSEA) and investigated the differences in PARK7 activity between cancer and paraneoplastic tissues in different tumors. Subsequently, we calculated and ranked the mean PARK7 gene activity from high to low in 33 cancers.

Analysis of potential association between PARK7 expression and immune-related factors

We used the ESTIMATE package, which predicts tumor purity and the presence of infiltrating mesenchymal/immune cells in the tumor, to calculate the stromal score and immune score for each case and generate three final scores: the stromal score (indicating the presence of stromal cells in the tumor tissue), the immune score (representing the infiltration of immune cells in the tumor tissue), and the tumor purity²⁶.

Subsequently, the abundance of immune cell infiltration in the low PARK7-expressing and high PARK7-expressing groups was estimated using the CIBERSORT algorithm, a deconvolution algorithm for assessing the proportion of 22 tumor-infiltrating lymphocyte subpopulations²⁷. We retained results with a p-value < 0.05. R > 0 indicates a positive correlation, i.e., the higher PARK7 expression, the more stromal cells or immune cells in the tumor sample.

To further investigate the relevant signaling pathways, GSEA was performed to identify differential pathways between the low PARK7-expressing and high PARK7-expressing groups from the Kyoto Gene and Genome Encyclopedia database. We show in the graphs the relevant signaling pathways that satisfy p < 0.05 and analyze the pathways with the top five highest normalized enrichment scores.

Previously published studies have shown that tumor mutational burden (TMB) and microsatellite instability (MSI) are closely associated with immune responses. Therefore, we investigated the correlation between these metrics and PARK7 expression. TMB refers to the total number of somatic gene coding, base substitution, gene insertion, or deletion errors detected per million bases. We calculated the TMB for each case by dividing the total number of mutations counted by the exome size (38 Mb was used as the exome size). The MSI score for each TCGA cancer case was obtained from a previously published study²⁸.

Finally, we explored the potential relationship between PARK7 expression and immunomodulators (immunosuppressants, immunostimulants, and MHC molecules) through the TISIDB website (http://cis.hku.hk/TISIDB/index.php). The four most relevant results are then highlighted.

Analysis of Immunotherapeutic Response

Our study included three relevant independent immunotherapy cohorts. Immunotherapeutic approaches generally produce four outcomes: complete response (CR), partial response (PR), progressive disease (PD), and stable disease (SD). We categorized patients who achieved CR or PR as responders and compared them to non-responders who demonstrated SD or PD. The Wilcoxon test was then used to determine the difference in PARK7 expression between the responder and non-responder groups.

Expression landscape of PARK7 in 33 human cancers

Based on the TCGA database, the expression levels of PARK7 in 33 tumor types were different, as shown in Fig. 1A. The five highest PARK7 expression levels in 33 tumors were DLBC (diffuse large B-cell lymphoma), THYM (thymoma), PRAD (prostate adenocarcinoma), GBM (glioblastoma multiforme) and UCEC (uterine corpus endometrial carcinoma), and the five lowest expression levels were CHOL (cholangiocarcinoma), KICH (kidney chromophobe), STAD (stomach adenocarcinoma), ESCA (esophageal carcinoma) and LAML (acute myeloid leukemia) (in a descending order by expression level). We compared the expression of PARK7 between normal and tumor samples and found that 16 of the 33 tumors (BLCA (bladder urothelial carcinoma), BRCA (breast invasive carcinoma), COAD (colon adenocarcinoma), ESCA, GBM, HNSC (head and neck squamous cell carcinoma), KICH, KIRC (kidney renal clear cell carcinoma), LIHC (liver hepatocellular carcinoma), LUAD (lung adenocarcinoma), LUSC (lung squamous cell carcinoma), PCPG (pheochromocytoma and paraganglioma), PRAD, STAD, THCA (thyroid carcinoma), UCEC) had significantly different expression levels (Fig. 1B). Higher expression levels were seen in tumor samples of BLCA, BRCA, COAD, ESCA, GBM, HNSC, LIHC, LUAD, LUSC, PRAD, STAD, UCEC than normal tissues, while the opposite was observed in tumor group of KICH, KIRC, PCPG, THCA. The results of PARK7 activity analysis were not identical to the results obtained from expression analysis (Fig. 1C). PARK activity was increased in BLCA, BRCA, GBM, KIRP (kidney renal papillary cell carcinoma), LIHC, LUAD, LUSC, PRAD and UCEC. However, compared to normal tissues, PARK7 activity of CHOL, KIRC, STAD was significantly lower in tumor samples.

Correlation of PARK7 expression level and clinical features

Figure 2 illustrated that for most of the 33 tumor types, PARK expression levels were not significantly correlated with clinical characteristics including as age, gender, and clinical stage. Older patients (≥ 60 years) with SARC (sarcoma) had an increased level of PARK7 expression, while the older patients of LGG (brain lower grade glioma), PARD and TYHM groups had significantly decreased levels of PARK7 expression. The expression level of PARK was higher in female LIHC patients than in male LIHC patients, and the opposite was observed in LUSC. PARK was significantly correlated with clinical stage in tumor group of KIRC and LIHC

The prognostic value of PARK7 in 33 tumors

Kaplan-Meier analysis showed that PARK7 expression was negatively correlated with OS in tumor types of UVM (uveal melanoma) (p = 0.034), GBM (p = 0.045), KIRC (p = 0.001) and LAML (p = 0.022). But in MESO (mesothelioma), patients with high expression of PARK7 had a higher overall survival rate (p = 0.012). The results of the multivariate COX regression model analyses revealed PARK7 as an prognostic factor in KICH (HR, 12.938, 95%CI: 2.938–70.104, p = 0.003), KIRP (HR, 0.476, 95%CI: 0.235–0.963, p = 0.039), LAML (HR, 2.457, 95%CI: 1.311–4.606, p = 0.005) and UVM (HR, 4.679, 95%CI: 1.692–12.939, p = 0.003) (Fig. 3A). Regarding DFS, PFS, and DSS, PARK7 expression was negatively correlated with the prognosis of patients in tumor groups of HNSC, ACC (adrenocortical carcinoma), LGG, KIRC, THYM and UCS (uterine carcinosarcoma). However, THCA patients, who had higher PARK7 expression, had better DFS and PFS. Unfortunately, multivariate analyses of PFS, DFS, and DSS all yielded insignificant results (Fig. 3B-D).

Association between PARK7 expression level and immune microenvironment

The immune score and stromal score were performed for 33 tumor types, and significant results were displayed in Fig. 4 (p < 0.05). PARK7 was positively correlated with immune scores of LGG, THYM, and UVM, but negatively correlated with stromal score of PARD. Figure 4 also summarized the relationship between PARK7 expression levels and immune cell infiltration. Notably, the results showed that PARK expression levels were negatively correlated with resting memory CD4 + T cells in KIRC, LIHC, PRAD, TGCT (testicular germ cell tumors), and THYM tumors. CD8 + T cells were elevated in the presence of high PARK7 expression levels, which were found in KIRC, THCA, and THYM tumor types.

Association between PARK7 expression level and immune check points, immune related genes

PARK7 expression was positively correlated with TMB in UCEC, STAD, READ (rectum adenocarcinoma) and LIHC, KIRP, but a negative correlation was observed in UVM, THYM and LAML. As for MSI, high PARK7 expression was found to lead to high MSI in 9 of 33 tumor types (DLBC, HNSC, KIRP, PARD, READ, SARC, SKCM (skin cutaneous melanoma), THCA and UCEC). However, in CESC (cervical squamous cell carcinoma and endocervical adenocarcinoma) and LUSC, high PARK7 expression is associated with low MSI (Fig. 5A, Fig. 5B). Figure 5C-E demonstrated the correlation of PARK7 expression with immune inhibitors, immune stimulators and MHC in 33 tumors. The top four strongest associations in each group were shown specifically according to rho absolute values. In PRAD and THCA, the immune inhibitor CD274 was negatively correlated with PRK7 expression, whereas PVRL2 in PARD and ADORA2A in TGCT were positively correlated with PRK7 expression. PARK7 expression was positively correlated with HLA-DQA2 in CHOL, HLA-A in SARC and KIRC, and the opposite result was found in HLA-DOA in PARD. 45 immune stimulators were analyzed and found that high PRK7 expression resulted in a decrease in TNFSF15 in THCA and PARD as well as TNFS14 in CHOL, but resulted in an increase in TNFSF9 in TGCT.

Association between PARK7 expression level and immunotherapeutic response

Across three independent cohorts, patients with low PARK7 expression in the GSE67501 cohort were more responsive to immunotherapy (p = 0.024), as shown in Fig. 6. There was no significant difference in PARK7 expression between responders and non-responders in the GSE78220 and Imvigro cohorts.

The biological pathways of PARK7 in cancers

The possible biological pathways of PARK in tumors were investigated by gene enrichment analyses. This study demonstrated the biological pathways of PARK7 in tumors which are closely associated with PARK7 expression ( Fig. 7). Low expression of PARK7 in LAML, PRAD, LIHC and UCEC enriched immune-related pathways such as autophagy regulation, toll-like receptor signaling pathway, TGFβ signaling pathway and RIG-I-like receptor signaling pathway, respectively. In UVM, chemokine signaling pathway, immune response activation, adaptive immune response and B cell activation were enriched in PARK7 high expression group.

The comprehensive research on PARK expression and activity in 33 tumor types reveals that PARK7 has different roles in different tumors. Not identical to the original assumption, PARK7 is not only an oncogenic factor, but also, has a protective effect in some tumors.

Consistent with previous studies, PARK7 was significantly overexpressed in BLCA²⁹, BRCA³⁰, COAD³¹, ESCA³², GBM³³, HNSC³⁴, LIHC³⁵, LUAD³⁶, LUSC³⁶, PRAD³⁷, STAD³⁸, UCEC³⁹ compared to normal tissues. Protein sequencing of thyroid tumor tissues by Srisomsap C⁴⁰ failed to find a characteristic difference in PAEK 7 between neoplastic and non-neoplastic conditions, in contrast to the findings in this paper. Our analysis of database data revealed that in thyroid tumors, PAEK7 expression was higher in normal tissues than in tumor tissues. The difference in results is probably related to the different methods of data collection. Protein expression level is more reflective of PARK7 tissue activity than transcript level, which is not available from public databases. Comparison of transcription level with PARK7 activity score revealed that transcription level matched activity of PARK7 in the tumor group of BLCA, BRCA, GBM, LIHC, LUAD, LUSC, PRAD and UCEC, suggesting that transcription levels are representative of PARK7 activity in these cancers. The inconsistency in PARK7 expression and activity may be due to alteration in post-transcriptional protein level or protein metabolism affecting PARK7 expression.

As for the prognostic value of PARK7, Kaplan-Meier analyses revealed that the low expression of PARK7 is associated with favourable overall survival outcomes in tumor group of UVM, GBM, KIRC, and LAML. COX regression model analyses suggest that PARK7 is an independent prognostic factor for KICH, LAML and UVM. A previous study has yielded similar findings⁴¹. The study on renal clear cell carcinoma did not directly yield a negative correlation with PARK7 and prognosis, but concluded that overexpression of PARK7 inhibits apoptosis in clear renal cell carcinoma cells⁴². Kaplan-Meie analysis and multivariate COX regression model analysis suggest that PARK7 plays a protective role in EMSO and KIRP, respectively. As far as we know, there are no studies on PARK7 and KIRP prognosis. Vavougios GD⁴³ studied BBS1, a interactor of PARK7, and found that elevated BBS1 is a favorable predictor of overall survival in malignant pleural mesothelioma, but it is not clear the relationship between BBSI and PARK7 expression and its involvement in malignant pleural mesothelioma. Altogether, these findings clearly indicate that PARK7 can be considered as a prognostic biomarker in pan-cancer.

Evaluation of PARK7 and immune infiltrating cells showed a negative correlation of PARK7 expression with resting memory CD4 + T cells and a positive correlation with CD8 + T cells in KIRC. Most CD8 + T cells are cytotoxic T lymphocytes and generally considered as the main force of host immune system to against tumor⁴⁴. Moreover, Davis D concluded that clear cell renal cell carcinoma patients with high CD8 + T cells had a significantly lower death rate⁴⁵. Combined with survival analysis, the findings of our research seem to be contradictory. However, our results are comparable to the other TCGA data analysis⁴⁶, which suggested that higher CD8 + T cell infiltration in clear cell renal cell carcinoma was associated with worse outcomes. These findings indicate the important role of CD8 + T cells in the immune microenvironment of renal clear cell carcinoma and the fact that increased CD8 + T cells do not simply mean a better prognosis. It was found that a lesser proportion of activated CD8 + cells and a greater proportion of exhausted CD8 + cells were associated with poor prognosis⁴⁷. We speculate that PARK7 may contribute to an increase in the amount of exhausted CD8 + cells. In fact, the immune microenvironment is dynamically regulating and interacting. It might be unreliable to assess the prognosis based on a single type of lymphocyte, which indicates the need for a comprehensive immune scoring system. Since PD-1 is mainly expressed on CD8 + cells, CD8 + cells are often used as a biomarker to assess the efficacy of immunotherapy, as are TMB⁴⁸ and MSI⁴⁹. PARK7 expression was positively correlated with TMB and MSI in group of UCEC and KIRP, implying that PARK7 expression may affect immunotherapy efficacy in UCEC and KIRP. We analyzed three independent cohorts, one of which found patients with low PARK7 expression were more sensitive to immunotherapy.

In UVM, patients with high PARK7 expression had higher immune scores, which was consistent with the pathway enrichment results. Gene enrichment analyses of UVM showed that chemokine signaling pathway, immune response activation, adaptive immune response and B cell activation were enriched in PARK7 high expression group. Furthermore, we found that the T cell activation pathway of cholangiocarcinoma and sarcoma was enriched in the PARK7 high expression group. The B cell differentiation pathway of breast cancer was enriched in the PARK7 low expression group. It was previously found that iTregs in mice lacking PARK7 failed to develop completely, and PARK7 played an important role in the differential regulation of iTregs and nTregs¹⁹. Combined with the results of the present study, PARK may be able to regulate the activation and differentiation of T and B lymphocytes, not only for Tregs. PARK7 is likely to be involved in the regulation of the immune response through these pathways. In other tumor types, PARK7-related immune pathways include toll-like receptor signaling pathway, TGFβ signaling pathway, RIG-I like receptor signaling pathway, and so on. Unfortunately, these pathways have not yet been proven.

In conclusion, through pan-cancer analysis of PARK7 in 33 human cancers, we found that PARK7 can be used not only as a prognostic biomarker for some cancers, but also as a potential biomarker for assessing the response to immunotherapy. These findings provide a rationale for subsequent prospective functional trials and may ultimately influence clinical decision in selected tumors.

PARK

Parkinson diseas

Tregs

regulatory T cells

TCGA

The Cancer Genome Atlas

GEO

The Gene expression omnibus

overall survival

DFS

disease-free survival

DSS

disease-specific survival

PFS

progression-free survival

hazard ratio

GSEA

gene set enrichment analysis

TMB

tumor mutational burden

MSI

microsatellite instability

complete response

partial response

progressive disease

stable disease

ACC

adrenocortical carcinoma

BLCA

bladder urothelial carcinoma

BRCA

breast invasive carcinoma

CESC

cervical squamous cell carcinoma and endocervical adenocarcinoma

CHOL

cholangiocarcinoma

COAD

colon adenocarcinoma

DLBC

lymphoid neoplasm diffuse large B-cell lymphoma

ESCA

esophageal carcinoma

GBM

glioblastoma multiforme

HNSC

head and neck squamous cell carcinoma

KICH

kidney chromophobe

KIRC

kidney renal clear cell carcinoma

KIRP

kidney renal papillary cell carcinoma

LAML

acute myeloid leukemia

LGG

brain lower grade glioma

LIHC

liver hepatocellular carcinoma

LUAD

lung adenocarcinoma

LUSC

lung squamous cell carcinoma

MESO

mesothelioma

ovarian serous cystadenocarcinoma:PAAD:pancreatic adenocarcinoma

PCPG

pheochromocytoma and paraganglioma

PRAD

prostate adenocarcinoma

READ

rectum adenocarcinoma

SARC

sarcoma

SKCM

skin cutaneous melanoma

STAD

stomach adenocarcinoma

TGCT

testicular germ cell tumors

THCA

thyroid carcinoma

THYM

thymoma

UCEC

uterine corpus endometrial carcinoma

UCS

uterine carcinosarcoma

UVM

uveal melanoma

Ethics approval and consent to participate

All datasets in the present study were downloaded from public databases, including TCGA and GEO databases. The patients involved in the databases have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. The current research follows the TCGA and GEO data access policies and publication guidelines. We can confirm that we did not use any tissue material in our study. Therefore patient consent was not required.

Consent for publication

Not applicable.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in TCGA data portal (https://portal.gdc.cancer.gov/) and GEO data portal （http://www.ncbi.nlm.nih.gov/geo）. TCGA includes current information on PARK7 in 33 tumors. GEO database Includes GSE78220, GSE67501 and IMvigor210 cohorts.

Competing interests

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.

Fundings

This work was supported by the National Natural Science Foundation of China (82003228) and Natural Science Foundation of Jiangsu Province (BK20201080).

Authors' contributions

Zeyuan Liu: Roles/Writing – original draft; Data curation; Investigation; Methodology

Qing Gao: Roles/Writing – original draft; Resources; Formal analysis; Methodology

Junjie Gu: Roles/Writing – original draft; Software; Validation; Investigation; Methodology

Xinchen Sun: Writing – review & editing; Conceptualization; Project administration; Funding acquisition

Xiaoke Di: Writing – review & editing; Conceptualization; Supervision; Funding acquisition

Acknowledgements

The authors would like to thank all participants in this study as well as individuals who contributed to the TCGA and GEO databases.

Huang Y, Cheung L, Rowe D, Halliday G. Genetic contributions to Parkinson's disease. Brain Res Brain Res Rev. 2004-08-01 2004;46(1):44-70.
Kawate T, Tsuchiya B, Iwaya K. Expression of DJ-1 in Cancer Cells: Its Correlation with Clinical Significance. Adv Exp Med Biol. 2017-01-20 2017;1037:45-59.
Bandyopadhyay S, Cookson MR. Evolutionary and functional relationships within the DJ1 superfamily. Bmc Evol Biol. 2004-02-19 2004;4:6.
Shen ZY, Sun Q, Xia ZY, Meng QT, Lei SQ, Zhao B, et al. Overexpression of DJ-1 reduces oxidative stress and attenuates hypoxia/reoxygenation injury in NRK-52E cells exposed to high glucose. Int J Mol Med. 2016-09-01 2016;38(3):729-36.
Shendelman S, Jonason A, Martinat C, Leete T, Abeliovich A. DJ-1 is a redox-dependent molecular chaperone that inhibits alpha-synuclein aggregate formation. Plos Biol. 2004-11-01 2004;2(11):e362.
Nagakubo D, Taira T, Kitaura H, Ikeda M, Tamai K, Iguchi-Ariga SM, et al. DJ-1, a novel oncogene which transforms mouse NIH3T3 cells in cooperation with ras. Biochem Biophys Res Commun. 1997-02-13 1997;231(2):509-13.
Bonifati V, Rizzu P, van Baren MJ, Schaap O, Breedveld GJ, Krieger E, et al. Mutations in the DJ-1 gene associated with autosomal recessive early-onset parkinsonism. Science. 2003-01-10 2003;299(5604):256-9.
Biosa A, Sandrelli F, Beltramini M, Greggio E, Bubacco L, Bisaglia M. Recent findings on the physiological function of DJ-1: Beyond Parkinson's disease. Neurobiol Dis. 2017-12-01 2017;108:65-72.
Hijioka M, Inden M, Yanagisawa D, Kitamura Y. DJ-1/PARK7: A New Therapeutic Target for Neurodegenerative Disorders. Biol Pharm Bull. 2017-01-20 2017;40(5):548-552.
McCoy MK, Cookson MR. DJ-1 regulation of mitochondrial function and autophagy through oxidative stress. Autophagy. 2011-05-01 2011;7(5):531-2.
Zhu ZM, Li ZR, Huang Y, Yu HH, Huang XS, Yan YF, et al. DJ-1 is involved in the peritoneal metastasis of gastric cancer through activation of the Akt signaling pathway. Oncol Rep. 2014-03-01 2014;31(3):1489-97.
Tabata Y, Nakanishi Y, Hatanaka KC, Hatanaka Y, Tsuchikawa T, Okamura K, et al. DJ-1 is a useful biomarker for invasive extrahepatic cholangiocarcinoma. Hum Pathol. 2018-06-01 2018;76:28-36.
Sitaram RT, Cairney CJ, Grabowski P, Keith WN, Hallberg B, Ljungberg B, et al. The PTEN regulator DJ-1 is associated with hTERT expression in clear cell renal cell carcinoma. Int J Cancer. 2009-08-15 2009;125(4):783-90.
Arnouk H, Merkley MA, Podolsky RH, Stöppler H, Santos C, Alvarez M, et al. Characterization of Molecular Markers Indicative of Cervical Cancer Progression. Proteomics Clin Appl. 2009-05-05 2009;3(5):516-527.
MacKeigan JP, Clements CM, Lich JD, Pope RM, Hod Y, Ting JP. Proteomic profiling drug-induced apoptosis in non-small cell lung carcinoma: identification of RS/DJ-1 and RhoGDIalpha. Cancer Res. 2003-10-15 2003;63(20):6928-34.
Zeng HZ, Qu YQ, Zhang WJ, Xiu B, Deng AM, Liang AB. Proteomic analysis identified DJ-1 as a cisplatin resistant marker in non-small cell lung cancer. Int J Mol Sci. 2011-01-20 2011;12(6):3489-99.
Zhu H, Liao SD, Shi JJ, Chang LL, Tong YG, Cao J, et al. DJ-1 mediates the resistance of cancer cells to dihydroartemisinin through reactive oxygen species removal. Free Radic Biol Med. 2014-06-01 2014;71:121-132.
Xu M, Wu H, Li M, Wen Y, Yu C, Xia L, et al. DJ-1 Deficiency Protects Hepatic Steatosis by Enhancing Fatty Acid Oxidation in Mice. Int J Biol Sci. 2018-01-20 2018;14(13):1892-1900.
Singh Y, Chen H, Zhou Y, Föller M, Mak TW, Salker MS, et al. Differential effect of DJ-1/PARK7 on development of natural and induced regulatory T cells. Sci Rep. 2015-12-04 2015;5:17723.
Zhang L, Wang J, Wang J, Yang B, He Q, Weng Q. Role of DJ-1 in Immune and Inflammatory Diseases. Front Immunol. 2020-01-20 2020;11:994.
Savage PA, Klawon D, Miller CH. Regulatory T Cell Development. Annu Rev Immunol. 2020-04-26 2020;38:421-453.
Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy. Cell. 2017-02-09 2017;168(4):707-723.
Bauer CA, Kim EY, Marangoni F, Carrizosa E, Claudio NM, Mempel TR. Dynamic Treg interactions with intratumoral APCs promote local CTL dysfunction. J Clin Invest. 2014-06-01 2014;124(6):2425-40.
Josefowicz SZ, Lu LF, Rudensky AY. Regulatory T cells: mechanisms of differentiation and function. Annu Rev Immunol. 2012-01-20 2012;30:531-64.
Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018-02-22 2018;554(7693):544-548.
Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013-01-20 2013;4:2612.
Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015-05-01 2015;12(5):453-7.
Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ, et al. Landscape of Microsatellite Instability Across 39 Cancer Types. JCO Precis Oncol. 2017-01-20 2017;2017.
Kawate T, Tsuchiya B, Iwaya K. Expression of DJ-1 in Cancer Cells: Its Correlation with Clinical Significance. Adv Exp Med Biol. 2017-01-20 2017;1037:45-59.
Le Naour F, Misek DE, Krause MC, Deneux L, Giordano TJ, Scholl S, et al. Proteomics-based identification of RS/DJ-1 as a novel circulating tumor antigen in breast cancer. Clin Cancer Res. 2001-11-01 2001;7(11):3328-35.
Wang W, Wang H, Xiang L, Ni T, Jin F, Deng J, et al. DJ‑1 is a new prognostic marker and predicts chemotherapy efficacy in colorectal cancer. Oncol Rep. 2020-07-01 2020;44(1):77-90.
Yuen HF, Chan YP, Law S, Srivastava G, El-Tanani M, Mak TW, et al. DJ-1 could predict worse prognosis in esophageal squamous cell carcinoma. Cancer Epidemiol Biomarkers Prev. 2008-12-01 2008;17(12):3593-602.
Guadagno E, Borrelli G, Pignatiello S, Donato A, Presta I, Arcidiacono B, et al. Anti-Apoptotic and Anti-Oxidant Proteins in Glioblastomas: Immunohistochemical Expression of Beclin and DJ-1 and Its Correlation with Prognosis. Int J Mol Sci. 2019-08-20 2019;20(16).
Xu S, Ma D, Zhuang R, Sun W, Liu Y, Wen J, et al. DJ-1 Is Upregulated in Oral Squamous Cell Carcinoma and Promotes Oral Cancer Cell Proliferation and Invasion. J Cancer. 2016-01-20 2016;7(8):1020-8.
Liu S, Yang Z, Wei H, Shen W, Liu J, Yin Q, et al. Increased DJ-1 and its prognostic significance in hepatocellular carcinoma. Hepatogastroenterology. 2010-09-01 2010;57(102-103):1247-56.
MacKeigan JP, Clements CM, Lich JD, Pope RM, Hod Y, Ting JP. Proteomic profiling drug-induced apoptosis in non-small cell lung carcinoma: identification of RS/DJ-1 and RhoGDIalpha. Cancer Res. 2003-10-15 2003;63(20):6928-34.
Hod Y. Differential control of apoptosis by DJ-1 in prostate benign and cancer cells. J Cell Biochem. 2004-08-15 2004;92(6):1221-33.
Li Y, Cui J, Zhang CH, Yang DJ, Chen JH, Zan WH, et al. High-expression of DJ-1 and loss of PTEN associated with tumor metastasis and correlated with poor prognosis of gastric carcinoma. Int J Med Sci. 2013-01-20 2013;10(12):1689-97.
Shu K, Xiao Z, Long S, Yan J, Yu X, Zhu Q, et al. Expression of DJ-1 in endometrial cancer: close correlation with clinicopathological features and apoptosis. Int J Gynecol Cancer. 2013-07-01 2013;23(6):1029-35.
Srisomsap C, Subhasitanont P, Otto A, Mueller EC, Punyarit P, Wittmann-Liebold B, et al. Detection of cathepsin B up-regulation in neoplastic thyroid tissues by proteomic analysis. Proteomics. 2002-06-01 2002;2(6):706-12.
Lago-Baameiro N, Santiago-Varela M, Camino T, Silva-Rodríguez P, Bande M, Blanco-Teijeiro MJ, et al. PARK7/DJ-1 inhibition decreases invasion and proliferation of uveal melanoma cells. Tumori. 2021-12-17 2021:3008916211061766.
Trivedi R, Dihazi GH, Eltoweissy M, Mishra DP, Mueller GA, Dihazi H. The antioxidant protein PARK7 plays an important role in cell resistance to Cisplatin-induced apoptosis in case of clear cell renal cell carcinoma. Eur J Pharmacol. 2016-08-05 2016;784:99-110.
Vavougios GD, Solenov EI, Hatzoglou C, Baturina GS, Katkova LE, Molyvdas PA, et al. Computational genomic analysis of PARK7 interactome reveals high BBS1 gene expression as a prognostic factor favoring survival in malignant pleural mesothelioma. Am J Physiol Lung Cell Mol Physiol. 2015-10-01 2015;309(7):L677-86.
Zitvogel L, Galluzzi L, Kepp O, Smyth MJ, Kroemer G. Type I interferons in anticancer immunity. Nat Rev Immunol. 2015-07-01 2015;15(7):405-14.
Davis D, Tretiakova MS, Kizzar C, Woltjer R, Krajbich V, Tykodi SS, et al. Abundant CD8+ tumor infiltrating lymphocytes and beta-2-microglobulin are associated with better outcome and response to interleukin-2 therapy in advanced stage clear cell renal cell carcinoma. Ann Diagn Pathol. 2020-08-01 2020;47:151537.
Li H, Mo Z. Prognostic Value of Metabolism-Related Genes and Immune Infiltration in Clear Cell Renal Cell Carcinoma. Int J Gen Med. 2021-01-20 2021;14:6885-6898.
Hu J, Chen Z, Bao L, Zhou L, Hou Y, Liu L, et al. Single-Cell Transcriptome Analysis Reveals Intratumoral Heterogeneity in ccRCC, which Results in Different Clinical Outcomes. Mol Ther. 2020-07-08 2020;28(7):1658-1672.
Chan TA, Yarchoan M, Jaffee E, Swanton C, Quezada SA, Stenzinger A, et al. Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic. Ann Oncol. 2019-01-01 2019;30(1):44-56.
Yamamoto H, Imai K. An updated review of microsatellite instability in the era of next-generation sequencing and precision medicine. Semin Oncol. 2019-06-01 2019;46(3):261-270.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Pan-cancer study of PARK7/DJ-1 as a biomarker for prognostic and immunological role in 33 human cancers

Status:

Version 1

Abstract

Figures

Background

Methods

Data collection

Clinical correlation between PARK7 expression and various cancers

Generation and investigation of PARK7 activity

Analysis of potential association between PARK7 expression and immune-related factors

Analysis of Immunotherapeutic Response

Results

Expression landscape of PARK7 in 33 human cancers

Correlation of PARK7 expression level and clinical features

The prognostic value of PARK7 in 33 tumors

Association between PARK7 expression level and immune microenvironment

Association between PARK7 expression level and immune check points, immune related genes

Association between PARK7 expression level and immunotherapeutic response

The biological pathways of PARK7 in cancers

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1