Oligoadenylate synthetases-like is a prognostic biomarker and therapeutic target in pancreatic ductal adenocarcinoma

Introduction

Pancreatic cancer (PC) is a highly malignant digestive tract tumor characterized by strong invasiveness, a high recurrence rate, and poor prognosis (1). According to statistics, more than 500,000 people are diagnosed with PC annually (2). Pancreatic ductal adenocarcinoma (PDAC) is the most common pathological type of PC and causes more than 95% of PC patients’ death (3). The prognosis of PDAC patients is unfavorable. The median survival time is only 6 months, whereas the 5-year survival rate is below 9% (4). Surgery represents the only chance of cure for PDAC patients in the early stage; however, only about 20% have the opportunity to undergo an operation owing to the presence of metastasis at the time of diagnosis (5).

In recent years, targeted therapy has made some progress in PDAC, bringing new opportunities for treating PC. It has been found that the occurrence of PDAC is chiefly related to uncontrolled tumor suppressor genes and oncogenes, such as mutations in KRAS, TPP53, CDLM2A, SMAD4, EGFR, and BRCA. Several drugs have been developed for these therapeutic targets (6). However, due to chemotherapeutic resistance, many PDAC patients cannot benefit from the current targeted therapy (7). Therefore, it is vital to find new therapeutic targets and biomarkers to predict outcomes.

The oligoadenylate synthase (OAS) proteins, identified as enzymes sensing exogenous nucleic acid and initiating antiviral pathways, are induced by interferon (IFN). The 2'-5' oligoadenylate synthetase-like (OASL) is a member of the OAS family. Unlike other members such as OAS1, OAS2, and OAS3, OASL lacks the activity of 2'-5' oligoadenylate synthetase activity. The function of OASL has been well characterized in different viral infection stages. In terms of tumor metastasis and growth, compared with wild-type mice, the OASL^−/− mice showed more produced more type I interferon-regulating transcription factor (IRF7), which inhibited tumor progression by enhancing the effector function of CD8 T + cells and NK cells. It is suggested that OASL can play an immune-related role in cancers (8,9). However, these studies are only limited to how OASL regulates the progression of tumors through the immune system. The role of OASL in tumor itself is rarely reported. Studies have shown that OASL is closely related to the proliferation of various tumors (10). In addition, OASL has been identified as a pivotal gene of trastuzumab-resistant gastric cancer (11). It has a good prognostic value in breast cancer (12) and enhances the efficacy of drug therapy in lung cancer (13). In conclusion, OASL may play a role as a proto-oncogene in tumor cells and promote the occurrence and development of cancer. However, the function of OASL in PDAC is rarely concerned.

Our research not only confirmed that it could be used as a prognostic biomarker for PDAC, but also explored its role in the occurrence and development of PDAC and further verified that OASL might be a potential therapeutic target in the future.

We present the following article in accordance with the REMARK reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-21-6618/rc).

Methods

Baseline information

Data were extracted from The Cancer Genome Atlas (TCGA) database and retrospectively analyzed by R software (version 3.6.3; https://cran.r-project.org/bin/windows/base/old/3.6.3/). The RNAseq data of fragments per kilobase per million (FPKM) format in PDAC were derived from TCGA, while Log2 transformation was performed. The molecule was OASL (ENSG00000135114). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

The expression of OASL and clinical characteristics in PDAC

The expression of OASL in a variety of cancers was analyzed using the Oncomine database (https://www.oncomine.org/resource/login.html), and its expression in PDAC was analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/index.html) (14,15). The ggplot2 package and basic package were used to reveal the relationship between OASL and clinical features. High and low expression of OASL was used as independent variable types. The dependent variable was clinical characteristics.

Receiver operating characteristic analysis

The pROC and ggplot2 package was used for visualization. The UCSC XENA (https://xenabrowser.net/datapages/) RNA seq data in TPM format of TCGA and GTEX uniformly were processed by the Toil process (16). Data were compared between samples after log2 conversion.

Kaplan-Meier survival curve

The survminer package and survival package were used for survival analysis visualization and analysis. We performed Log2 conversion of RNAseq data in FKM format in TCGA. We then verified the results using the survival module in GEPIA. The parameter settings were as follows: gene was OASL, group cutoff was median, the primary endpoint including overall survival (OS), disease-specific survival (DSS), and disease-free survival (DFS), were used as indicators to evaluate the correlation between OASL expression and prognosis.

Gene Set Enrichment Analysis (GSEA)

GSEA was used to associate genes with possible pathways. A false discovery rate (FDR) of <0.25 and P adjust <0.05 were used as the criteria for judging statistically significant. We chose the enrichment pathways with obvious statistical significance.

TIMER database analysis

Tumor Immune Estimation Resource (TIMER; https://cistrome.shinyapps.io/timer/) is a public resource widely utilized to assess immune infiltration in multiple cancers from the TCGA database (17). The immune correlation module was used for analysis.

Cell culture and transfection

We used 3 human PDAC cells for in vitro experiments. All cell lines were purchased from Procell Life Science & Technology (Hyderabad, India) and authenticated by short tandem repeat (STR) profiling. All cell lines were cultured with Dulbecco’s modified Eagle medium (DMEM; Thermo Fisher Scientific, Shanghai, China) and incubated at 37 °C with 5% CO₂. Interference of OASL was performed using small interfering RNA (si-OASL-NC; si-OASL-1, si-OASL-2, and si-OASL-3) sequences synthesized by Tsingke Biotechnology (Beijing, China), the sequences are as follows: si-OASL-1 (5'-GTGAAACATCGGCCAACTA-3'), si-OASL-2 (5'-CATCACGGTCACCATTGT-3'), si-OASL-3 (5'-GGTGGTCCTGGAAATTTCT-3'). Lipofectamine 3000 (Invitrogen, Shanghai, China) was used for transfection. Relevant experiments were carried out 48 h after transfection.

MTT assay

Cell viability was detected by 3-(4,5-dimethylathiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. About 5×10³ cells were seeded on 96-well plates. We added 20 μL MTT solution followed by incubation for 4 h at each point (24, 48, 72, and 96 h). The medium was aspirated, and 150 μL of dimethyl sulfoxide (DMSO) was added to each well to disrupt the cells and dissolve the intracellular MTT dyes. Last, the absorbance was measured at 490 nm on a microplate reader. The MTT assays were performed in triplicate (for each experiment) and 3 independent biological repeats.

Transwell assay

Cell migration was detected by transwell assay. We seeded 1.5×10⁴ cells into the upper chamber followed by culture with a serum-free medium. A total of 0.75 mL serum-containing medium was added to the lower chambers. After incubation for 24 h, the invaded cells were fixed with paraformaldehyde and stained with crystal violet and photographed in 3 random fields under a microscope (10×10). Each experiment was repeated 3 times.

Flow cytometric analysis

Apoptosis was detected by annexin V apoptosis kit (Vazyme, Jiangsu, China). Fluorescence-activated cell sorting (FACS) was performed on a BD Accuri^® C6 Plus [Becton, Dickinson, and Co. (BD) Biosciences, Franklin Lakes, NJ, USA] and analyzed by FlowJo software (https://www.flowjo.com/). In a nutshell, 1×10⁶ cells were collected and resuspended after adding 100 μL binding buffer. Next, 100 μL binding buffer containing 5 μL annexin V-FIFC and 5 μL propidium iodide (PI) staining solution was added. Last, 400 μL binding buffer was added to the culture tube and analyzed within 1 h using flow cytometry.

Western blot

Total protein was obtained utilizing radioimmunoprecipitation assay (RIPA) lysis buffer (Servicebio, Wuhan, China). Protein concentration was quantified using the bicinchoninic (BCA) assay (Solarbio, Beijing, China). After adding loading buffer, samples were boiled for 5 min. Then, 20 μg protein was added to each lane, divided by 8–15% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), and then transferred to polyvinylidene fluoride (PVDF) membranes, which were blocked with 5% no-fat dry milk in Tris-buffered saline with 0.1% Tween 20 (TBST) for 2 h. The diluted primary antibodies against OASL (1:1,000) and glyceraldehyde 3-phosphate dehydrogenase (GADPH; 1:10,000) were incubated with the membrane overnight at 4 °C. After cleaning with TBST for 10 min, it was incubated with corresponding antibodies for 2 h and the membrane was cleaned 3 times with TBST. Last, electrochemiluminescence (ECL, Thermo, China) was applied for visualizing the results.

Statistical analysis

Part of the statistical analysis was carried out by default by network resources, and the others were carried out by R software and basic package (ggplot2, pROC, survminer, survival, rms). The Student’s t-test was used for comparison between the 2 groups (si-OASL-NC vs. si-OASL-1, si-OASL-NC vs. si-OASL-2, si-OASL-NC vs. si-OASL-3). Only P<0.05 was considered statistically significant.

Results

Clinical characteristics

Participants’ demographics and clinical variables related to PDAC are shown in Table 1. A total of 178 patients were involved in this study. The T stage included 7 T1 stage (4%), 24 T2 stage (13.6%), 142 T3 stage (13.6%), and 3 T4 stage (1.7%). The N stage included 50 N0 stage (28.9%), and 23 N1 stage (71.1%). The M stage included 79 M0 stage (94%) and 5 M1 stage (6%). In the pathologic stage, stage I, stage II, stage III, stage IV were 21 cases (12%), 146 cases (83.4%), 3 cases (1.7%), and 5 cases (2.9%), respectively. The median age was 65 years, ranging from 57 to 73 years. Other clinical information is displayed in Table 1.

OASL was highly expressed in PDAC

The Oncomine database was searched to obtain OASL expression levels in order to generally indicate the OASL in normal tissues and cancer. The expression of OASL was increased in PDAC and other cancers (Figure 1A). The GEPIA database was used for further verification (P<0.05, Figure 1B). The area under the curve (AUC) of OASL was 0.984 (Figure 1C).

Figure 1 OASL expression is significantly higher in PDAC than in normal tissues. (A) OASL expression in different human cancer tissues compared with normal tissues by the Oncomine database. (B) OASL expression in PDAC is higher than adjacent normal tissues in GEPIA. (C) ROC curve. *, P<0.05. CNS, central nervous system; PAAD, pancreatic adenocarcinoma; FPR, false positive rate; OASL, oligoadenylate synthetases-like; PDAC, pancreatic ductal adenocarcinoma; GEPIA, Gene Expression Profile Interactive Analysis; ROC, receiver operating characteristic.

Correlation between OASL expression and clinical characteristics

The 178 PDAC patients were divided into 2 groups according to the median expression of OASL. The P value of T stage, pathologic stage, and histologic grade was 0.010, 0.007, and <0.001, respectively (Table 2). Logistics regression analysis of PDAC showed that the P value of T stage (T1&T2 vs. T3&T4) was 0.004, the P value of pathologic stage (stage I vs. stage II) was 0.005 (Figure 2, Table 3).

Figure 2 OASL expression in PDAC was associated with clinical characteristics. (A) T stage. (B) Pathologic stage. ***, P<0.001. OASL, oligoadenylate synthetases-like; PDAC, pancreatic ductal adenocarcinoma.

High expression of OASL associated with poor prognosis

To further confirm the prognostic value of OASL, participants were divided into high and low groups according to the median expression of OASL. The results showed that high expression of OASL was significantly associated with poor OS (P<0.01) and DSS (P<0.01) in PDAC (Figure 3A,3B). We used the survival analysis module of GEPIA to verify the prognostic information of OASL in TCGA. The results of OS were consistent (P<0.01) (Figure 3C), while there was no significant difference in DFS (Figure 3D). Next, univariate and multivariate Cox regression analysis were used to explore the prognostic factors of PDAC. Besides, radiation therapy, primary therapy outcome and histologic grade also influenced tumor progression (P<0.05) (Table 4). The prognostic nomogram (Figure 4) and forest plot (Figure 5) were constructed based on Cox regression.

Figure 3 The Kaplan-Meier survival curves for different OASL levels. (A,B) The OS and DSS curves of PDAC patients with high vs. low OASL expression in TCGA database. (C,D) Survival curves of OS and DFS in PDAC cohort from GEPIA database. OASL, oligoadenylate synthetases-like; TPM, transcripts per million; OS, overall survival; DSS, disease-specific survival; PDAC, pancreatic ductal adenocarcinoma; DFS, disease-free survival; TCGA, The Cancer Genome Atlas; GEPIA, Gene Expression Profile Interactive Analysis.

Figure 4 Nomogram for predicting the survival probability. OASL, oligoadenylate synthetases-like; CR, complete remission; PD, progressive disease; SD, stable disease; PR, partial remission.

Figure 5 Multivariate Cox analysis of OASL expression and other clinicopathological factors. T stage, N stage, radiation therapy, primary therapy outcome, residual tumor, and OASL are independent prognostic factors. OASL, oligoadenylate synthetases-like; CI, confidence interval; HR, hazard ratio.

OASL-related pathways based on GSEA and correlated with immune infiltration

The OASL-related signaling pathways based on GSEA were enriched in OASL expression phenotypes. The top 5 pathways with notable statistical significance were GPCR-ligand binding, Neuronal system, Class A/1 (rhodopsin-like receptors), G-Alpha/1 signaling events, and Leishmania infection (Figure 6). Next, we found that OASL expression is significantly correlated with neutrophil in the TIMER database (r=0.189, P=1.32e-02; Figure 7A). In addition, the scan module was used to test the effect of different copy states of OASL on immune infiltration (Figure 7B).

Figure 6 GSEA of OASL in PDAC. (A) GPCR-ligand binding. (B) Neuronal system. (C) Class A/1 (rhodopsin-like receptors). (D) G-Alpha/1 signaling events. (E) Leishmania infection were enriched in OASL-related PDAC. GSEA, Gene Set Enrichment Analysis; OASL, oligoadenylate synthetases-like; PDAC, pancreatic ductal adenocarcinoma; NES, normalized ES; FDR, false discovery rate.

Figure 7 Correlation analysis of OASL expression and infiltration levels of immune cells in PDAC tissues using the TIMER. (A) OASL expression level has significant positive correlations with infiltrating levels of Neutrophils (r=0.189, P=1.32e-02). (B) The infiltration level of various immune cells under different copy numbers of OASL in PDAC. *, P<0.05. **, P<0.01. ***, P<0.001. OASL, oligoadenylate synthetases-like; TPM, transcripts per million; PAAD, pancreatic adenocarcinoma; PDAC, pancreatic ductal adenocarcinoma; TIMER, Tumor Immune Estimation Resource.

Knockdown of OASL inhibits cells proliferation, invasion, and promotes apoptosis of PDAC

Subsequently, to explore the effect of OASL interference, we performed at least 3 independent cellular experiments. We employed chemically synthesized siRNA to knock down the expression of OASL. The western blot assays showed si-OASL-2 and si-OASL-3 have higher efficiency (Figure 8A). The MTT assays demonstrated that cell viability was sharply reduced in si-OASL groups (Figure 8B). Similarly, the Transwell assay demonstrated that cells migration decreased significantly after OASL knockdown (Figure 8C). Flow cytometry showed that knockdown of OASKL promoted PDAC cells apoptosis (Figure 8D). These results confirm that OASL can be used as a therapeutic target to delay the malignant biological behavior of PDAC.

Figure 8 Knock-down of OASL inhibits cells proliferation, invasion and promotes apoptosis of PDAC cells. (A) The OASL protein expression was significantly decreased upon si-OASL transfection. (B) Cell viability was measured by MTT assay (Panc-1, si-NC vs. si-OASL-1. P<0.05. si-NC vs. si-OASL-2. P<0.05. Mia paca-2, si-NC vs. si-OASL-1. P<0.05. si-NC vs. si-OASL-2. P<0.05. Aspc-1, si-NC vs. si-OASL-1. P<0.05. si-NC vs. si-OASL-2. P<0.05). (C) The invasive ability of si-OASL groups appeared sharply reduced in the Transwell assay. Crystal violet-stained invasive cells were captured using an inverted microscope at ×100 magnification. (D) Obviously more apoptosis was observed in si-OASL groups. Experiments were repeated a minimum of 3 times. OASL, oligoadenylate synthetases-like; PDAC, pancreatic ductal adenocarcinoma; MTT, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide.

Discussion

The mortality of PC, in particular PDAC, is high (18). Due to the specific anatomical location of the pancreas, most patients are diagnosed at an advanced stage and have surpassed their only opportunity for a cure by surgical resection (19). Moreover, the aggressive local invasion and metastatic progression of PDAC are associated with a poor prognosis (20). Currently, TNM stage and hematologic markers (CA199, CEA, CA125) are used to evaluate the prognosis of patients. However, its effectiveness and accuracy are not high (21). Therefore, there is urgently needed for developing effective biological targets for the prediction and treatment of PDAC.

The OAS families, of which OASL is a member, have been found correlated with multiple diseases, including infections, autoimmune disorders, and cancers (22-24). In particular, it has been reported that OASL is significantly related to tumor proliferation (10). Our data highlighted the prognostic value and therapeutic target of OASL in PDCA. In this study, the expression of OASL and clinical characteristics were intimately connected. More importantly, patients with high expression of OASL had shorter OS and DSS. In addition, OASL expression was an independent prognostic factor of PADC patients' prognosis. Next, based on GSEA, OASL was related to pathways including GPCR-ligand binding, Neuronal system, Class A/1 (rhodopsin-like receptors), G-Alpha/1 signaling events, and Leishmania infection. In the accurate prognosis prediction of clear cell carcinoma, OASL was identified as a novel gene (25). In addition, OASL could be used as the key to evaluation of the sensitivity of cisplatin chemotherapy in cervical cancer (26). Targeted OASL can improve drug efficacy in lung cancer (13). These studies indicate that OASL plays an essential role in the occurrence of cancers.

Previous studies have confirmed that the malignant progression of PDAC is highly associated with the tumor microenvironment (27,28). We found that OASL was closely related to neutrophil infiltration, which was consistent with prior studies (12,29). Neutrophils secrete a cocktail of tumor-promoting factors, such as hepatocyte growth factor (HGF), matrix metalloproteinases (MMPs), reactive oxygen species (ROS), and promoted tumor angiogenesis, invasion, and metastasis (30). Moreover, some chemokines produced by neutrophils also lead to tumorigeneses, such as CCL2, CCL3, CCL19, and CCL 20 (31). We speculate that OASL may promote the function of neutrophils recruited to the tumor site, fail to mount an anti-tumor response, and thus lead to PDAC progression (32).

High invasiveness is the main cause of death in PDAC patients. The enhancement of cell viability, invasion, and migration are the main factors leading to tumor recurrence and metastasis. After the knockdown of OASL, we found that the proliferation and invasion of cells were inhibited, and the apoptosis rate increased. We speculate that the high expression of OASL may promote the malignant biological behavior of PDAC cells, resulting in the poor prognosis of patients. Nevertheless, further research is needed on which pathways OASL affects the phenotype of cells and whether it can be used as a target of chemoresistance.

In conclusion, our study shows that OASL can be used as a prognostic biomarker and a potential therapeutic target in PDAC.

Conclusions

The expression of OASL has been associated with immune infiltrating cells. Our study investigated the role of OASL in PDAC and provided novel prognostic biomarkers and therapeutic targets for PDAC.

Acknowledgments

Funding: This work was supported by Natural Science Foundation of Shandong Province, China (ZR2017MH032); Key Technology Research and Development Program of Shandong (2019GSF108065); Natural Science Foundation of Shandong Province, China (ZR2020MH256); Key Technology Research and Development Program of Shandong (2019GSF108254); the Medical Health Science and Technology Project of Shandong Provincial Health Commission (2019WS386); The National Natural Science Foundation of China (Grant No. 81900731) and ECCM Program of Clinical Research Center of Shandong University (No. 2021SDUCRCB010).

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://atm.amegroups.com/article/view/10.21037/atm-21-6618/rc

Data Sharing Statement: Available at https://atm.amegroups.com/article/view/10.21037/atm-21-6618/dss

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-21-6618/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Jones)