The original data were downloaded from the TCGA database (https://cancergenome.nih.gov). TIMER2 (http:///timer.cistrome.org/) (16) was utilized to detect OAS1 expression. Based on the TCGA database, we downloaded the transcriptome data of the pan-cancer patients and the corresponding clinical data, and collated the clinical data using the R software. Then, bioinformatics analysis was used to explore the expression level of OAS1 in cancer tissues and normal tissues. UALCAN database (http://ualcan.path.uab.edu//analysisprot.html) was utilized to assess protein expression levels of OAS1 in pan-cancers. The GEPIA database (http://GEPIAcancer-pku.cn) (17) was utilized to detect the expression level of OAS1 in different tumor stages.

The Human Protein Atlas (HPA) (http:///www.proteinatlas.org/) database was utilized to evaluate the protein expression levels of OAS1 in tumor and adjacent tissues. Immunohistochemical images of six tumor tissues and corresponding normal tissues were obtained from HPA database, including BRCA (breast cancer), COAD (colon cancer), THCA (thyroid cancer), LUAD (lung adenocarcinoma), HNSC (head and neck squamous cell carcinoma) and UCEC (endometrial cancer).

We detected the relationships between OAS1 expression and molecular subtypes and immune subtypes from the TISIDB database (18). TISIDB is a public, open and multiduty database to analyze the interactions between tumors and the immune system.

In this study, we used the kmplot database (http://kmplot.Thecom/analysis/) (19) and GEPIA databases to perform the OS and DFS analysis. These two databases are public databases that are often used to analyze gene expression levels as well as survival time of patients (20).

The ROC curve was utilized to detect the diagnostic value of OAS in various tumors using R software (R-4.0.9). The closer the AUC value is to 1, the better the reference value for diagnosis.

In this study, to analyze the biological processes and signaling pathways that regulated by OAS1 in tumors, the Linked Omics database (http://www.linkedomics.org) (21) was utilized to perform KEGG analysis and GSEA analysis using R software (R-4.0.9). Pearson’s correlation was used to analyze the relation between OAS1 and co-expression genes.

The data of cancer and normal tissues of pan-cancer were downloaded from TCGA. R software v4.0.9 was utilized to generate a heat map of Spearman correlations to explore the correlation between multiple immune checkpoint genes and OAS1 expression in pan-cancers.

GDC (https://portal.gdc.camcer.gov/) database was utilized to obtain the Simple Nucleotide Variation data, and calculated the TMB of each cancer types using R software (R-4.0.9).

Lung cancer cell lines (H1975 and A549) and prostate cancer cell lines (PC-3 and C4-2) were purchased from ATCC. Cell lines were cultured in DMEM (Gibco, Carlsbad, CA, USA) containing 10% fetal bovine serum (BI) and maintained at 37°C with 5% CO₂. Allowed the cells to grow to ~70% confluency, and 1×10⁵cells/well were inoculated to 6-well plates (Corning, NY, USA) one day before transfection (22). The riboFECTTMCP Transfection Kit (ribobio, Guangzhou, China) was utilized to transfect siRNA into cells. The siRNA sequences of OAS1 used were: negative siRNA negative control (NC) (5’-TTCTCCGAGCGTCACGT-3’), siRNA-OAS1 #1 (5’-CCGCAUGCAAAUCAA CCAUTT-3’), siRNA-OAS1 #2 (5’-GCUUCCGAGGUAGCUCCUATT-3’), and siRNA-OAS1 #3 (5’-GGUGGAGACCCAAAGGGUUTT -3’).

96-well plates were used to inoculate cancer cells at 1500 cells/well after cells transfected with OAS1-siRNA, and cells were cultured for the indicated days. Each well of 96-well plates were added 10μl Cell Counting Kit 8 (CCK-8, Dojindo) and incubated for 1.5 hours (23). Microplate reader (Bio-Rad, Gaithersburg, MD, USA) was used to measure the absorbance at 450 nm.

The cells were transfected with OAS1 specific siRNA for 72 h and then lysed with RIPA (medchemexpress), then protein lysates were boiled at 100˚C with 5×loading buffer for 6 min. The cell lysates were separated using 10% SDS-PAGE and transfer to the PVDF membranes (Millipore, USA) (24). The PVDF membranes were blocked with 5% defatted milk for 2 h. The anti-OAS1 (Santa Cruz, CA, sc-374656, 1:2000 dilution), anti-GAPDH (BOSTER, China, A00227-1, 1:4000 dilution) were used as primary antibody. HRP-conjugated goat anti-mouse (BOSTER, China, BA1051, 1:5000 dilution) was used as secondary antibody. ImageJ software was used for stripe analysis.

Cancer cells were digested and resuspension, and washed twice with PBS after cells transfected with siRNA for 72 h. Cells were fixed with 70% precooled ethanol at 4°C for 1 day. The next day, the cells were washed three times with PBS, then stained with Cell Cycle Kit (Beyotime, China, C1052) (25). The cells were then analyzed by a flow cytometry (Beckman Coulter Quanta SC System).

Cells were seeded in wells of 6-well plates with 15×10⁴cells/well and incubated in DMEM containing 10% FBS. The OAS1-siRNAs were used to impair OAS1 expression for 48 h, then cells were treated with 10 μM cisplatin for 24h. Finally, cells were digested and re-suspension with 1×bingding buffer, then, cells were treated with Annexin V-FITC (10 μl) and PI (5μl) (Absin, China) (26).

Cancer cells were transfected with OAS1 specific siRNA for 48 h. Then cells were digested and suspension in DMEM with 0.1% BSA, 4×10⁴cells/well was inoculated into the upper chambers (Costar, MA, USA), the lower chambers were added 500μl DMEM plus 10% FBS. Cells were allowed to migrate for 12 h in incubator. Use a cotton swab to wipe away the cells in the upper chamber before staining. The migrated cells were fixed with methanol and 0.1% crystal violet was utilized to stain cells (27), the migrated cells were photographed and counted using a microscope (Olympus).

Student’s t-test was used to evaluate alterations of OAS1 expression levels in tumor tissue and normal tissue. Kruskal test was used for multiple sets of samples. Spearman or Pearson test was used to analyze the correlation between the two variables. Kaplan-Meier curves were used to perform survival analysis, Cox regression was used to assess the HR and p value. The R language software (R-4.0.9) was utilized to perform statistical analysis in this study. P<0.05 was considered statistically significant. pan-cancers

We used the TIMER 2 database to analyze the expression level of OAS1 in pan-cancer. We found an inconsistent expression of OAS1 expression in pan-cancer. In BLCA (Bladder Urothelial carcinoma), BRCA, CHOL (Cholangiocarcinoma), ESCA (Esophageal carcinoma), HNSC, KIRC (Kidney Renal Clear Cell carcinoma), KIRP (Kidney Renal Papillary Cell carcinoma), LUAD, THCA and UCEC, OAS1 expression was significantly higher in tumor tissues than that in adjacent normal tissues, whereas OAS1 expression was significantly lower in COAD and KICH (Kidney Chromophobe) than that in adjacent normal tissues ( Figure 1A ). Meanwhile, in order to comprehensively analyze the expression of OAS1 in pan-cancer, the expression level of OAS1 in tumor tissues and matched normal tissues were also examined using TCGA database. ESCA, BLCA, BRCA, HNSC, KIRC, KIRP, LUAD, READ (Rectum adenocarcinoma), STAD (Stomach adenocarcinoma) and THCA show high expression of OAS1 in tumor tissues than that in matched normal tissues, whereas OAS1 mRNA level in KICH was significantly lower than that in the matched normal tissues ( Figure 1B ).

We further used the UALCAN database to explore OAS1 protein expression levels in tumor tissue and the corresponding normal tissue. OAS1 protein levels were up-regulated in breast cancer, COAD, ovarian cancer, renal clear cell carcinoma, endometrial cancer, lung cancer, pancreatic cancer, HNSC and glioblastoma, but down-regulated in liver cancer ( Figure 1C ). We also analyzed immunohistochemical results using HPA database. In BRCA, HNSC, HUAD, THCA, and UCEC, tumor tissues show higher expression level of OAS1 than that in normal tissues, but OAS1 was lower in tumor tissues than that in normal tissues in COAD ( Figure 2 ).

We also examined the relationship between OAS1 expression and tumor pathological stage using the GEPIA tool. Results showed that OAS1 expression was associated with several tumor pathologic stage, including BLCA, LUAD, PAAD (Pancreatic adenocarcinoma) and SKCM (Skin Cutaneous Melanoma) ( Figure 1D ).

We further examined the expression levels of OAS1 in tumor patients with different molecular subtypes in pan-cancers. We observed that the expression levels of OAS1 were different in diverse molecular subtypes of eight cancer types. In BRCA, OAS1 express was higher in the molecular subtype of LumB than others ( Figure 3A ). In HNSC, OAS1 showed highest expression level in the molecular subtype of Basal ( Figure 3B ). In KIRP, OAS1 showed higher expression level in the molecular subtype of C1 and C2a than C2b and C2c-CIMP ( Figure 3C ). In LGG, OAS1 express was highest in the molecular subtype of Classic-like than others ( Figure 3D ). In LUSC, OAS1 showed higher expression level in the molecular subtype of basal and secretory than classic and primitive ( Figure 3E ). In OV, OAS1 express was highest in the molecular subtype of Classic-like than others ( Figure 3F ). In STAD, OAS1 showed higher expression level in the molecular subtype of EBV and HM-indel than CIN, GS and HM-SNV ( Figure 3G ). In UCEC, OAS1 express was highest in the molecular subtype of CN-HIGH than others ( Figure 3H ). These data indicate that the expression of OAS1 was associated with molecular typing of tumors.

In order to explore the correlation between OAS1 and tumor immune subtypes, the TISIDB database was used to explore the expression of OAS1 in different immune subtypes of pan-cancers. We found different expression of OAS1 in 6 different immune subtypes of tumors (C1: Wound Healing, C2: IFN-γ Dominant, C3: Inflammatory, C4: Lymphocyte Depletion, C5: Immunologically Quiet, C6: TGF-β Dominant). OAS1 was found highest in BLCA ( Figure 4A ) and KIRP ( Figure 4G ) with C6 signature. OAS1 was highest in BRCA ( Figure 4B ), COAD ( Figure 4C ), HNSC ( Figure 4E ), KIRC ( Figure 4F ), LUAD ( Figure 4H ), READ ( Figure 4I ), STAD ( Figure 4J ), THCA ( Figure 4K ), and UCEC ( Figure 4L ) with the interferon gamma (IFN-γ) dominant (C2) signature. OAS1 was highest in ESCA ( Figure 4D ) with the lymphocyte depleted (C4) signature. Reports show that C2 signature is correlated with less favorable outcomes, C4 and C6 are correlated with the least favorable outcome. These data suggest that OAS1 may be correlated with tumor immune status and as an oncogene in multiple tumors.

Next, we focused on the correlation between OAS1 expression and patient prognosis. According to the expression level of OAS1, tumor patients were divided into high and low OAS1 expression groups. Patients with high expression of OAS1 showed poor OS (overall survival), including ACC (Adrenocortical carcinoma) (P=0.0077), LGG (Brain lower grade glioma) (P=0.025), LIHC (P=0.025), LUAD (P=0.0081), PAAD (P=0.047), and UVM (Uveal Melanoma) (P=0.0035). Whereas patients with high expression of OAS1 showed longer OS, including BLCA (P=0.0098) and SKCM (P=0.000064) ( Figure 5A ). In addition, high OAS1 expression was associated with poor DFS (disease-free survival), including LGG (P=0.0049), LUAD (P=0.019), PRAD (Prostate adenocarcinoma) (P=0.027), and UVM (P=0.0087) ( Figure 5B ).

ROC curves were used to evaluate the diagnostic value of OAS1 in pan-cancer. We observed that OAS1 had a certain reference value in the diagnosis of 15 cancer types (AUC> 0.7), including BLCA (AUC = 0.722) ( Figure 6A ), BRCA (AUC = 0.890) ( Figure 6B ), CESC (AUC = 0.890) ( Figure 6C ), CHOL (AUC = 0.716) ( Figure 6D ), ESCA ( Figure 6E ), GBM (AUC = 0.969) ( Figure 6F ), HNSC (AUC = 0.726) ( Figure 6G ), KICH (AUC = 0.738) ( Figure 6H ), KIRC (AUC = 0. 911) ( Figure 6I ), KIRP (AUC = 0.951) ( Figure 6J ), LGG (AUC = 0.869) ( Figure 6K ), LUAD (AUC = 0.775) ( Figure 6L ), OV (AUC = 0.919) ( Figure 6M ), PAAD (AUC = 0.983) ( Figure 6N ), TGT (AUC = 0.943) ( Figure 6O ). Among them, OAS1 predicted GBM, KIRC, KIRP, OV, and PAAD with a high accuracy (AUC> 0.9).

To investigate the biological processes and molecular mechanisms of OAS1 regulation in pan-cancer, we identified a gene-sets including top200 genes that positively co-expressed with OAS1 using GEPIA database, which were used to perform GSEA and KEGG annotation analyses. The top 5 pathways of KEGG analysis included metabolism of xenobiotics by cytochrome P450, retinol metabolism, pentose and glucuronate interconversions, and ascorbate and aldarate metabolism ( Figure 7A ). GSEA_GO annotation was used to assess the OAS1-related signaling pathways that were differentially activated in pan-cancer. Data showed that OAS1 affects multiple gene ontology (GO) pathways, including negative regulation of the viral life cycle and process, response to type I interferon, defense response to virus, antigen processing and presentation of exogenous peptides antigen via MHC class, and chemokine receptor binding ( Figure 7B ).

GSEA_KEGG analysis suggested that OAS1 was involved in type I diabetes, proteasome, cytokine receptor interaction, NK cell-mediated cytotoxicity, graft-versus-host disease, NOD like receptor signaling pathways, processing and presentation of antigen ( Figure 7C ). GSEA_REATOME analysis indicated that several immune functional terms were enriched in the gene sets co-expressed with OAS1, including interferon signaling, antigen processing cross presentation, chemokine receptors binding chemokines ( Figure 7D ). These results suggest that OAS1 was involved in antiviral response and the immune response.

Our above data suggest that OAS1 may be involved in the immune response in pan-cancer, which indicated that OAS1 might be involved in antitumor immune response. Here, we further explored the correlation between OAS1 expression and the immune checkpoint genes. Our results showed that OAS1 expression in BRCA, GBM, HNSC, KIRC, LGG, LUSC, SARC, SKCM, TGCT, THCA, and UVM were positively associated with various immune checkpoint genes, such as BTLA, CD244, CD247, CD96, CSF1R, CTLA 4, IDO 1, IL 10 and LAG 3, and other immune checkpoint genes ( Figure 8A ). Tumor mutational burden (TMB) is a conspicuous biomarker correlated with the immunotherapy response. The correlation between the OAS1 expression and TMB was examined. High OAS1 patients had higher TMB in ESCA, LGG, PAAD and STAD, whereas patients with high OAS1 expression exhibited lower TMB in UVM, TGCT and THCA ( Figure 8B ). These data indicate that OAS1 may be involved in the tumor immunotherapy response.

To further examine the role of OAS1 in cancer cells, LUAD (A549 and H1975) and PRAD (PC-3 and C4-2) cell lines were selected for functional experiments. The OAS1-targeting siRNAs were used to silence OAS1 expression, and the inhibition efficiency of OAS1-targeting siRNAs were determined by Western blot. OAS1-targeting siRNAs were significantly reduced OAS1 expression after OAS1 siRNA #SI1 and #SI2 transfection ( Figure 9A and Supplementary Figure 1A ). The effects of OAS1 on cell proliferation was examined by CCK-8 assay. We observed that the silence of OAS1 significantly inhibited the proliferative activity of A549 and H1975 cells ( Figure 9B ), as well as PC-3 and C4-2 cells ( Supplementary Figure 1B ). The effects of silence OAS1 expression on the cell cycle and apoptosis in LUAD cells were determined by flow cytometry. The cell cycle of A549, H1975, PC-3 and C4-2 cells arrested in the G2/M phase after OAS1 silencing ( Figure 9C and Supplementary Figure 1C ). In addition, silence of OAS1 significantly enhanced cisplatin-induced apoptosis both in LUAD cells and PRAD cells ( Figure 9D and Supplementary Figure 1D ), but impaired cell migration ( Figure 9E and Supplementary Figure 1E ). These data suggest that OAS1 plays a role in regulating cell cycle, apoptosis and cell migration in cancer cells.