PRKAA1 predicts prognosis and is associated with immune characteristics in gastric cancer

doi:10.21203/rs.3.rs-2116841/v1

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Research Article

PRKAA1 predicts prognosis and is associated with immune characteristics in gastric cancer

https://doi.org/10.21203/rs.3.rs-2116841/v1

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Journal Publication

published 24 Jul, 2023

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Purpose

PRKAA1 is the α-subunit of 5-AMP-activated protein kinase. This study aimed to investigate the role of PRKAA1 expression in multiple clinical parameters, the overall survival rate, blood indexes, and immune infiltration in gastric cancer (GC) patients.

Methods

We investigated PRKAA1 expression data of in GC patients using an ELISA, proteinatlas, ualcan, and GEPIA. PRKAA1 expression was associated with immune cell infiltration, and immune cell types were analyzed with the TIMER, DICE, and proteinatlas databases. We compared the level of PRKAA1 expression based on the clinical features of GC patients (n = 345). GC patients were divided into two groups based on PRKAA1 expression, and the lymphocyte subsets, overall survival rate, and clinical parameters were compared with peripheral blood mononuclear cell and biochemical indexes.

Results

PRKAA1 was highly expressed in the serum of GC patient compared with that of healthy individuals. GC patients with distant metastases, a later TNM stage, and stage IV in UICC exhibited higher PRKAA1 expression. PRKAA1 expression was significantly correlated with circulating T cells. The proteinatlas and DICE database results confirmed that PRKAA1 was closely associated with T cells in a single cell cluster. Furthermore, GC patients with low PRKAA1 expression had better OS rates.

Conclusion

PRKAA1 may serve as a potential prognostic biomarker for GC and have an association with immune infiltrates.

gastric cancer

PRKAA1

immune characteristics

survival outcomes

Globally, gastric cancer (GC) represents one of the most common and deadly malignancies, and is a serious threat to human health (Luebeck, et al. 2013, Li, et al. 2021). Although various therapeutic methods, including surgery, chemotherapy and molecular-targeted therapy, were widely used to treat GC clinically (Li, He, Fan, Zhang, Xu and Xu 2021, Lutz, et al. 2012, Zhao, et al. 2020), the 5-year survival rate of advanced GC remains less than 20% (Thomassen, et al. 2014, Kahraman and Yalcin 2021). Recently, systemic treatment with immune checkpoint inhibitors (ICIs) has been successful for the treatment of GC (Kono, et al. 2020, Zhao, et al. 2022), improving both the progression free survival and overall survival of patients (Xie, et al. 2021, Das and Johnson 2019). However, the populations benefiting from immunotherapy have not been clearly defined; therefore, it is imperative to develop an immune prognostic predictor that can monitor progression and immune characteristics in GC.

AMP-activated protein kinase (AMPK) alpha 1 (PRKAA1) is a conserved serine/threonine kinase, also known as an energy sensor (Dial, et al. 2018, Zhu, et al. 2014). Multiple studies have demonstrated that PRKAA1 can serve as a tumor promoter to inhibit apoptosis and promote the proliferation of GC cells (Fuchs, et al. 2018, Zhang 2019). Additionally, PRKAA1 polymorphisms are associated with both the clinical pathological characteristics, risk, and progression of GC (Chen, et al. 2018, Dargiene, et al. 2018, Wang and Brautigan 2013, Fogarty, et al. 2016). Preclinical data has shown that AMPKα1 plays a primary role in the immunosuppressive activities induced by tumor myeloid-derived suppressor cells (MDSC), overcoming MDSC-driven tumor T cell dysfunction, further boosting the effectiveness of immunotherapy (Trillo-Tinoco, et al. 2019). Clinically, PRKAA1 has been reported to exhibit dynamic changes during immunotherapy for the treatment of colorectal cancer (Fogarty, Ross, Vara Ciruelos, Gray, Gowans and Hardie 2016, Lee, et al. 2014) However, the clinical and immune data evaluating the relevance of PRKAA1 alterations in GC patients and its correlation with the immune characteristics remain insufficient.

In this study, we explored the relationship between PRKAA1 expression and the clinical features of GC using plasma collected from GC patients and healthy individuals. We further explored the relationship between lymphocyte subpopulations in the peripheral blood and PRKAA1 expression. Our findings indicate that PRKAA1 plays an essential role in regulating immune infiltration in GC and may represent a novel powerful prognostic biomarker for immunotherapy in GC.

Patient Inclusion Criteria and Blood Samples

This was a retrospective single-institution cohort study. This study was approved by the local Ethics Committee of the Zhejiang Cancer Hospital (IRB-2021-154). We collected blood samples from 169 healthy people and 345 GC patients between 2015 and 2022. Patients with a history of other malignant tumors were excluded.

A BD EDTA Routine blood tube (Anticoagulant tube) was used to collect the plasma samples. A heparin tube was used for lymphocyte subset detection. Serum samples were collected in a tube prefilled with a coagulant for the ALB and LDH tests. These three blood samples were simultaneously collected from healthy individuals and GC patients. All of the tubes were purchased from Zhejiang Gongdong Medical Technology (No. 10 Beiyuan Road, Huangyan District, Tai zhou, Zhejiang province).

ELISA for PRKAA1 expression

A commercial ELISA test kit (Rui xin, OM15209) was used to evaluate the level of PRKAA1 expression in the plasma samples. Testing was performed according to the standardized protocol in the ELISA test kit. Corresponding plasma samples (50 mL) and standard model proteins (50 mL) were added to 96-well plates. Next, 100 mL of an HRP-labeled antibody was added and the 96-well plates were incubated for 1 h at 37°C. The plates were washed three times in washing buffer. The substrate mixture (100 mL) was added and the plate was analyzed in microplate reader at OD450.

Analysis of the TIMER database, Database of Immune Cell Expression (DICE) and The human protein atlas

Tumor Immune Estimation Resource (TIMER, https://cistrome.shinyapps.io/timer/) was used to predict the correlation between PRKAA1 expression and purity of GC. DICE (https://dice-database.org/genes/PRKAA1) was used to evaluate the expression of PRKAA1 in various immune cells. Furthermore, PRKAA1 expression in each immune cell cluster was also shown in the DICE database. The level of PRKAA1 expression in different cancers was shown in the human protein atlas (https://www.proteinatlas.org/ENSG00000132356-PRKAA1/single+cell+type), which also displayed the level of PRKAA1 expression in various immune cells according to cell type.

Flow cytometry

PBMCs (1 × 10⁶ cells/mL) were collected in PBS (50 mL) supplemented with 0.5% BSA (Term Fisher, USA) and incubated with anti-human monoclonal antibodies for 15 min. Anti-human monoclonal antibodies included anti-CD45/CD56/CD19/, anti-CD45/CD4/CD8/CD3, anti-CD4, CD3, anti-CD45RO, anti-CD8, anti-CD38, and anti-CD45RA (Beckman Coulter, USA), as shown in supplementary Table 1. CXP analysis software and a Beckman Coulter FC 500 flow cytometer was used to analyze the results (Janjigian, et al. 2020, Picard, et al. 2020, Qin, et al. 2016).

Statistical analysis

Statistical analyses were performed using SPSS 19.0 software (IBM, Armonk, NY). Patient survival from 2015 to 2019 was confirmed by telephone, and was analyzed using the Kaplan-Meier method with a log-rank test. P values < 0.05 were considered statistically significant. Furthermore, Kaplan-Meier Plotter (http://www.kmplot.com) was also used to support the effect of PRKAA1 (high vs low expression) on the survival time of GC patients. Python was used to reflected the clinical features and parameters of the GC patients.

Patient characteristics and high PRKAA1 expression in GC patients

We initially evaluated the levels of PRKAA1 transcription in various cancers by proteinatlas and the GEPIA database, and found that PRKAA1 mRNA expression was higher in GC (Fig. 1a and b). In total, 345 GC patients (235 males and 110 females) and 169 healthy individuals (80 males and 89 females) were included in this study. Table 1 and Fig. 1c summarize the characteristics of healthy individuals and GC patients. As shown in Fig. 1c and Table 1, the mean age was 66 (range: 26 to 86) years for GC patients, and 49 (range: 21 to 88) years for healthy individuals. The clinical features of the GC patients (e.g., UICC tumor stage, pathological grade, lymph node involvement, distant metastasis, Her2 expression, CEA expression, CA19-9 expression, and clinical stage) are summarized in Table 2 and Fig. 1c. Furthermore, the GEPIA and UALCAN databases were used to analyze the differences in the levels of PRKAA1 expression between normal stomach tissues and GC. PRKAA1 expression was found to be remarkably lower in the adjacent normal tissue compared with the GC tissue (Fig. 1d). To confirm the expression of PRKAA1, we analyzed the levels of PRKAA1 expression in the serum and found significantly higher expression levels in the serum of GC patients (n = 345) compared to that of healthy individuals (n = 169) (Fig. 1e).

Table 1

Comparison between GC patients and healthy individuals with clinical parameters
Baseline characteristics	Gastric cancer patients	Healthy individuals
Number	345	169
Age, years, n(%)
≤ 60	108 (31.30)	115 (68.04)
＞60	237 (68.69)	54 (31.95)
P₅₀ (P₂₅, P₇₅)	66 (57, 72)	49 (36.5, 62)
Sex, n(%)
Male	235 (68.11)	80(47.33)
Female	110 (31.88)	89 (52.66)
CEA, P₅₀ (P₂₅, P₇₅)^a	1.65 (0.67, 3.58)	0.89(0.50, 1.52)
CA19-9, P₅₀ (P₂₅, P₇₅)^a	13.58 (8.19, 27.55)	10.38 (6.23, 16.02)
PRKAA1, P₅₀ (P₂₅, P₇₅)^a	1.59 (1.07, 2.39)	1.39 (0.91, 3.22)
a: median and interquartile range

Table 2

Baseline characteristics of GC patients in this study
Baseline characteristics	Gastric cancer patients (n = 345)
Smoking n(%)	Yes				No
Smoking n(%)	136 (39.42)				209 (60.57)
Drinking n(%)	Yes				No
Drinking n(%)	109 (31.59)				236(68.40)
Family history of cancer n(%)	Yes				No
Family history of cancer n(%)	88 (25.50)				263 (76.23)
Distant metastases n(%)	Yes				No
Distant metastases n(%)	70				275
PRKAA1 n(%)	High expression				Low expression
PRKAA1 n(%)	172 (49.85)				173 (50.14)
CEA n(%)	High expression				Low expression
CEA n(%)	171 (49.56)				174 (50.43)
CA19-9 n(%)	High expression				Low expression
CA19-9 n(%)	173 (50.14)				172 (49.85)
T classification n(%)	pT1		pT2		pT3		pT4
T classification n(%)	20 (5.79)		39 (11.30)		119 (34.49)		167 (48.40)
N classification n(%)	pN0		pN1		pN2		pN3
N classification n(%)	81 (23.47)		44 (12.75)		69 (20.00)		151 (43.76)
UICC stage n(%)	I		II		III		IV
UICC stage n(%)	37		62		135		111
Her₂ IHC n(%)	-	+		++		+++		No test
Her₂ IHC n(%)	145	62		49		26		63

Association between PRKAA1 and clinical outcomes in GC patients

The association between PRKAA1 expression and the clinical characteristics of GC patients, including age, sex, grade, family history of cancer, TNM grade, distant metastasis, smoking, and drinking habits was analyzed. Among the clinical parameters, PRKAA1 was remarkably higher in stage T3-4, N2-3, and stage IV (according to UICC staging criteria) (Fig. 2a); however, PRKAA1 expression exhibited limited differences in the subgroups of Her2 expression (Fig. 2a). Remarkably, PRKAA1 was highly expressed in GC patients with metastases, whereas no differences were observed in PRKAA1 expression in GC patients regarding age, sex, smoking history, family history, and drinking history in GC patients (Fig. 2b-c). These results suggest that PRKAA1 expression was closely correlated with T classification, N classification, and UICC stages.

PRKAA1 is associated with T cells in single cell types

We further analyzed PRKAA1 expression in various of immune cells, and the relationship between PRKAA1 expression and immune infiltration was also investigated. The DICE database was used to analyze the level of PRKAA1 expression in various types of immune cells. As shown in Fig. 3a, PRKAA1 was highly expressed in both CD8 + and CD4 + T cells. The infiltration levels of different somatic copy number alterations for PRKAA1 was also analyzed. The level of diploid/normal PRKAA1 were similarly expressed at high levels in CD8 + and CD4 + T cells (Fig. 3b). Next, we analyzed PRKAA1 expression in various single cell types, and found that PRKAA1 was specifically highly expressed in T cells (Fig. 3c), which was consistent with the results of the proteinatlas database analysis (Fig. 3d). Furthermore, the TIMER database was used to explore the association between PRKAA1 expression and immune infiltration in GC. Levels of PRKAA1 expression were positively corrected with the tumor purity (Cor = 0.07) in GC (Fig. 3e).

The relationship between PRKAA1 expression and circulating T cells

We measured the peripheral blood indexes in healthy individuals and GC patients, including T cell (CD3+), helper/effector T cell (CD3 + CD4+), suppressor/cytotoxic T cell (CD3 + CD8+) cells, CD4/CD8 ratio (CD3 + CD4+/CD3 + CD8+), natural killer cell (CD3-CD56+), B cell (CD3-CD19+), NKT cell (CD3 + CD56+), naive helper T cell (CD4 + CD45RA+), memory helper T cell (CD4 + CD45RA-), CD4 + naive/memory ratio (CD4 + CD45RA+/CD4 + CD45RA-), memory helper T cell (CD4 + CD45RO+), activated CD8 cell (CD8 + CD38+), and biochemical indexes, ALB and LDH (Supplementary Fig. 1). Next, we investigated PRKAA1 expression based on the abundance of various types of circulating cells in the blood of GC patients and healthy individuals. Compared with healthy individuals, differential PRKAA1 expression was associated with the presence of T cells in GC patients (Fig. 4a). PRKAA1 expression was divided into two groups (PRKAA1 high and low expression) according to p50 (Table 1). T cells (CD3+), memory helper T cells (CD4 + CD45RA-), memory helper T cells (CD4 + CD45RO+), helper/effector T cells (CD3 + CD4+), suppressor/cytotoxic T cells (CD3 + CD8+), and the CD4/CD8 ratio were highly expressed in GC patients with PRKAA1 low expression (Fig. 4b-c). Additionally, our findings demonstrated that PRKAA1 expression was significantly negatively correlated with T cell (CD3+) (Cor=-0.5089, P < 0.0001), memory helper T cell (CD4 + CD45RA-) (Cor=-0.1619, P = 0.1150), memory helper T cell (CD4 + CD45RO+) (Cor=-0.1481, P = 0.1500), helper/effector T cell (CD3 + CD4+) (Cor=-0.1898, P = 0.0640) and suppressor/cytotoxic T cell (CD3 + CD8+) cell (Cor=-0.2956, P = 0.0035) levels in GC patients (Fig. 4b-c).

Prkaa1 Low Expression Was Correlated With Decreased Survival Of Gc Patients

We analyzed the prognostic value of PRKAA1 expression based on the survival of GC patients from 2015 to 2019. As indicated in Fig. 5a, high PRKAA1 expression was related to a slightly shorter overall survival. In addition, high levels of CEA and CA19-9 expression were also associated with slightly shorter overall survival of GC patients (Fig. 5a). Furthermore, the Kaplan-Meier database (http://kmplot.com/) also indicated that PRKAA1 expression significantly affected GC prognosis, with significantly lower overall survival in GC patients with PRKAA1 high expression, consistent with the findings in Fig. 5b. Furthermore, similar results were observed in the UALCAN tools (http://ualcan.path.uab.edu/cgi-bin/ualcan-res.pl) analysis, which were used to further confirm the correlation between the level of PRKAA1 and GC patient survival (Fig. 5c).

PRKAA1 is the α-subunit of 5-AMP-activated protein kinase (AMPK), which functions as an energy sensor and plays a key role in maintaining cellular metabolic homeostasis (Roa, et al. 2018, Wang, et al. 2019). Previous studies have shown that the down-regulation of PRKAA1 induces metabolic abnormalities and can lead to the development of tumors (Krishan, et al. 2014). Although several studies have found an association between PRKAA1 and GC risk (Chen, Jiang, He, Tang, Wang, Chen, Lu and Lu 2018), there are limited studies that have evaluated the expression of PRKAA1 in the serum of GC patients. This study revealed that PRKAA1 was highly expressed in the serum of GC patients, compared with healthy individuals, suggesting that PRKAA1 exhibits a specific and sensitive advantage in the diagnosis of GC. Whether the expression of PRKAA1 is related to clinical outcome and pathological characteristics of GC patients remains unknown. In this study, we first demonstrated that high PRKAA1 expression was associated with TNM stage and stage I and metastases in GC patients.

T cells play a critical role in anti-tumor immune responses (Fu and Jiang 2018). Our results showed that PRKAA1 was highly expressed in immune cells, including CD4 + and CD8 + T cells. Moreover, according to the analysis of the DICE and proteinatlas databases, PRKAA1 was specifically highly expressed in the T cell cluster; however, the role of PRKAA1 in T-cell function has been poorly studied. Furthermore, we also demonstrated that the expression of PRKAA1 was closely associated with T cell (CD3+), memory helper T cell (CD4 + CD45RA-), memory helper T cell (CD4 + CD45RO+), and helper/effector T cell (CD3 + CD4+) in the peripheral blood of GC patients. It is important to note that PRKAA1 expression also showed a negative correction with the percentage of circulating T cells. Therefore, the mechanism by which PRKAA1 regulates immune cells in GC warrants further investigation.

Previous studies have indicated that PRKAA1 plays a crucial role in cancer risk and progression by regulating inflammation and metabolism (Griffiths, et al. 2020). In this study, the clinical cohort analysis confirmed an association between PRKAA1 and GC prognosis. The findings of the Kaplan-Meier database analysis was consistent with our analysis that a lower expression of PRKAA1 promoted the overall survival of GC patients. Various of studies have confirmed that PRKAA1 phosphorylation in Thr172 indicates AMPK pathway activation (Carling, et al. 2008). Although our study revealed a potential role of PRKAA1 expression in GC tumor immunology and its prognostic value, the level of PRKAA1 phosphorylation in Thr172, known as activation of the AMPK pathway, requires further investigation in the context of GC tumor immunology and progression.

In summary, our results demonstrate that PRKAA1 might play an essential role in regulating immune infiltration in GC and may represent a novel promising prognostic biomarker for immunotherapy of GC.

Funding This work was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY21H200001 and No. Q21H160017. Project of Science and Technology Department of Zhejiang Province No. 2022500399. Zhejiang province public welfare technology application research project No. 2020KY058 and No. 2021KY082.

Competing Interests The authors have no relevant financial or non-financial interests to disclose.

Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yongyi Chen, Siyu Chen, Jing Zhu and Qiong Yu. The first draft of the manuscript was written by Yongyi Chen, Shaoxue Yang and Songxiao Xu. All authors commented on previous versions of the manuscript. Songxiao Xu and Yongyi Chen supervised the project. All authors read and approved the final manuscript.

Data Availability The datasets generated during the current study are available from the corresponding author on reasonable request.

Ethics approval This study was approved by the local Ethics Committee of the Zhejiang Cancer Hospital (IRB-2021-154).

Consents to participate Informed consent was obtained from all individual participants included in the study.

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Supplementary Table 1 is not available with this version.

No competing interests reported.

FigS1.jpg
Supplementary Fig. 1. Peripheral blood indexes in healthy individuals and GC patients. a Peripheral blood indexes in healthy individuals. b Peripheral blood indexes in GC patients.

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published 24 Jul, 2023

Read the published version in Functional & Integrative Genomics →

Editorial decision: Major revision
08 Apr, 2023
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Reviewers agreed at journal
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Reviewers agreed at journal
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You are reading this latest preprint version

PRKAA1 predicts prognosis and is associated with immune characteristics in gastric cancer

Status:

Journal Publication

Version 1

Abstract

Purpose

Methods

Results

Conclusion

Figures

Introduction

Materials And Methods

Patient Inclusion Criteria and Blood Samples

ELISA for PRKAA1 expression

Analysis of the TIMER database, Database of Immune Cell Expression (DICE) and The human protein atlas

Flow cytometry

Statistical analysis

Results

Patient characteristics and high PRKAA1 expression in GC patients

Association between PRKAA1 and clinical outcomes in GC patients

PRKAA1 is associated with T cells in single cell types

The relationship between PRKAA1 expression and circulating T cells

Prkaa1 Low Expression Was Correlated With Decreased Survival Of Gc Patients

Discussion

Declarations

References

Supplementary Table 1

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1