LncRNA RP3-525N10.2-NFKB1-PROS1 triplet-mediated low PROS1 expression is an onco-immunological biomarker in low-grade gliomas: a pan-cancer analysis with experimental verification

Abstract

Background: Glioma is the most common cancer in the central nervous system, and low grade gliomas are notorious for many types of tumors and heterogeneity. PROS1 not only plays an important role in the blood coagulation system, and recent studies have found that it was correlated with the development of tumors, especially related to tumor immune infiltration. However, the study of underlying role and mechanism of PROS1 in gliomas, especially in low-grade gliomas, is almost absent.

Methods: We integrated the information of patients with LGG in The Cancer Genome Atlas (TCGA) cohort and Chinese Glioma Genome Atlas (CGGA) cohort. Then, we systematically demonstrated the differences and prognostic prognosis value of PROS1 based on multi-omics analyses. In addition, Cell counting kit-8 (CCK-8) assay, colony formation assay, 5-Ethynyl-2'-deoxyuridine (EdU) incorporation assay, and Transwell assays were performed to evaluate cell proliferation and invasion. qRT-PCR and immunohistochemistry were used to evaluate the expression of PROS1 in LGG.

Results: Various bioinformatics approaches revealed that PROS1 was a valuable prognostic marker and may influence tumour development via distinct mechanisms, including expression of DNA methyltransferase, RNA modification, and DNA mismatch repair system genes, copy number variation, single nucleotide variation frequency, genomic heterogeneity, cancer stemness, DNA methylation, and alternative PROS1 splicing. Our analyses indicated that the long non-coding RNA RP3-525N10.2 may "decoy" or "guide" the transcription factor NFKB1 and prevent its association with PROS1, thereby reducing PROS1 expression and improving poor LGG prognosis. PROS1 expression was also closely associated with tumour infiltration by immune cells, especially tumour-associated macrophages, as well as the expression of various immune checkpoint inhibitors, immunomodulators, and immune cell markers.

Conclusion: long non-coding RNA RP3-525N10.2-NFKB1-PROS1 triplet-mediated PROS1 expression could serve as a biomarker for cancer diagnosis, prognosis, therapy selection, and follow-up in LGG patients.

Keywords: Carcinogenesis; Immunohistochemistry; Long noncoding rna; Macrophages; Transcription factors; Transwell assays; Tumor microenvironment.

Fig. 1

Differential expression levels of PROS1 in different cancers and PROS1-related differentially expressed genes (DEGs). A, B The expression of PROS1 in multi- types of human cancer based on TCGA cancer and GTEx database, C, D Differential expression levels of PROS1 in LGG, E The ROC curve to test the value of PROS1 to identify LGG tissues was created, F, G Volcano plots of the DEGs and heat map showing the up-regulated and down-regulated top 5 DEGs

Fig. 2

mRNA and protein expression of PROS1 in glioma patients. A Immunohistochemical staining of PROS1 was performed in 15 paired tumor samples compared with adjacent normal samples.Scare bars, 50 mM. B Realtive mRNA expression of PROS1 in 30 paired tumor samples compared with adjacent normal samples. C Realtive mRNA expression of PROS1 in 4 cell lines (NHA, U87, U251 and T98G). ***p < 0.001

Fig. 3

Correlation analysis between the expression of PROS1 and the expression levels of methyltransferases, RNA Modifications Genes, DNA Mismatch Repair System (MMRs) Genes, Copy Number Variation (CNV), and Single nucleotide variation (SNV) frequency. A Correlation analysis of PROS1 expression with that of 4 DNA methyltransferases (DNMT1 was colored red, DNMT2 was colored blue, DNMT3a was colored green, and DNMT3b is colored purple) in pan-cancer. B Correlation analysis of PROS1 expression with 44 RNA modifications genes (m1A(10)、m5C(13)、m6A(21)) in pan-cancer. C Correlation analysis of PROS1 expression with with the mutation levels of 5 MMR genes (MLH1, MSH2, MSH6, PMS2, and EPCAM) in pan-cancer. D Lollipop plot displaying mutation distribution and protein domains for PROS1 in cancer with the labeled recurrent hotspots. E Correlation analysis of PROS1 expression with CNV alternation in three groups (Neutral = 491, Loss = 14, Gain = 3) tested by by kruskal method. F Oncoplot displaying the somatic landscape of low grade gliomas cohort

Fig. 4

The DNA methylation and Alternative Splicing (AS) of PROS1 in LGG. A The visualization between the DNA methylation level and PROS1 expression. B–D The Kaplan–Meier survival of the 3 promote (cg14753809, cg05897638, and cg24305970) methylation of PROS1. E–J Two Alternate promoters (PROS1_AP_65674 and PROS1_AP_65675) were found in TCGA SpliceSeq database that related with poor prognosis

Fig. 5

The prognostic value of PROS1 in LGG. A Forest plots showing the HRs related to PROS1 expression in pan-cancer. B–H Kaplan–Meier curves for patients stratified by different expression levels of PROS1 in TCGA (B, C), CGGA, GEPIA database (E), OncoLnc (F), PrognoScan database (G, H). I–J 1-, 2- and 3 year overall survival ROC curves based on risk score in TCGA (I)and CGGA cohorts (J)

Fig. 6

Subgroups prognostic, univariate, multivariate Cox regression,and nomogram analysis of PROS1 in LGG. A–E Kaplan–Meier curves of high and low PROS1 expression in subgroups, including female (A), Age > 40 years (B), 1p/19q non-codeletion (C), SD&PD (D), and astrocytoma (E). F–G The univariate F and multivariate Cox regression G analysis of PRSO1 in LGG patients. H–I Risk factor associations diagram H and Sankey diagram I were used to depicted the overall prognostic trend and living status of the inner relationship. J The nomogram for predicting the probability of 1-, 3-, and 5- year OS for LGG patients. K Calibration plots of the nomogram for predicting the probability of OS at 1, 3, and 5 years

Fig. 7

Identification of PROS1-Interacting Genes and Proteins. A The gene–gene interaction network of PROS1 was constructed using GeneMania. B The PPI network of PROS1 was generated using STRING. C A heat map shows the correlations between PROS1 and icoagulation related genes in LGG

Fig. 8

Identification LncRNA-TF-PROS1 Triplet in LGG. A The TF-PROS1 regulatory network. B The expression correlation between predicted TFs and PROS1 in LGG. C–I The expression and prognosis analysis of TFs in LGG Using GEPIA database. J The LncRNA-NFKB1 regulatory network. K–L The expression and prognosis analysis of possible LncRNAs in LGG Using GEPIA database

Fig. 9

Significantly enriched GO, KEGG and GSEA annotations of PROS1 related genes in LGG. A–D The top 10 significant terms of BP (A), MF (B), CC C and KEGG D enrichment analysis were depicted by circular plot. E, F The top 8 pathways of GSEA enrichment analysis were depicted by ridge plot, including KEGG E and Reactome pathways (F)

Fig. 10

PROS1 increased the proliferation, invasion of GBM cells in vitro. A qRT-PCR assays were applied to analyse the expression level of PROS1 after transfection by sh PROS1 or PROS1 overexpression vector for 24 h in U87 or U251 cells. B Cell viability of U87 or U251 cells after knocking down or overexpressing PROS1 was determined using CCK8 assays. C Colony formation assay were performed in transfected U87 or U251 cells to evaluate cell proliferation ability. D Transwell assays were performed in transfected U87 or U251 cells to evaluate cell invasion ability (Magnification: × 100). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Error bars indicate mean ± SD

Fig. 11

5-Ethynyl-2’-deoxyuridine (EdU) staining to detect cell proliferation. A U87 cells were treated with EdU for 6 h prior to click reaction. B U251 cells were treated with EdU for 6 h prior to click reaction. Data analysis was performed to calculate the signal intensity in EdU-positive cells based on individual DAPI signal and displayed in the right graph. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.Error bars indicate mean ± SD

Fig. 12

The relationship of immune cell infiltration and tumor micro-environment with PROS1 level in LGG. A, B The correlation between 6 immune related cells and PROS1 expression A and prognosis (B) in LGG patients. C, D The relationship between PROS1 expression and the immune infiltration analyzed by ssGSEA and depicted by Lollipop Chart and correlation diagrams. E The infiltration level of various immune cells under different copy numbers of PROS1 in LGG. F–H The correlation of PROS1 expression level with immune score, stromal score, as well as ESTIMATE score

Fig. 13

Correlation Between PROS1 expression and Immune checkpoint inhibitors and immunomodulators. A Correlation between PROS1 and 150 immunomodulators ((chemokine (41), receptor (18), MHC (21), Immunoinhibitor (24) and Immunostimulator (46)). B Correlation analysis between PROS1 expression in Pan-cancer and immune checkpoint gene expression. C–G Correlation of SEMA3F expression with CD274 (C), CTLA4 (D), HAVCR (E), IDO1 (F), and PDCD1 G expression in LGG. *Significant correlation P < 0.05, **Significant correlation P < 0.01, ***Significant correlation P < 0.001

Fig. 14

Correlation between PROS1 expression and therapy outcome in clinical studies of immune checkpoint blockade. A Bar plot showing the biomarker relevance of PROS1 compared to standardized cancer immune evasion biomarkers in immune checkpoint blockade (ICB) sub-cohorts. The area under the receiver operating characteristic curve (AUC) was applied to evaluate the predictive performances of the test biomarkers on the ICB response status. B, C Heatmap of PROS1 associations with lymphocyte-mediated tumor killing in CRISPR screens C and outcomes in ICB sub-cohorts (B). D–H 4 glioma cohorts (ca00037@PRECOG, Nutt_Glioma@PRECOG, TCGA, and GSE16011@PRECOG) expression levels of MXD3 were correlated with OS and the level of CTL. I–J CTL expression levels of GSE16011@PRECOG cohort was related with overall survival in PROS1 mRNA expression I and copy number alteration data (J). K 4 drugs were the peak drug candidates based on DSigDB database drug molecules

Fig. 15

Graphical abstract of the model of RP3-525N10.2–NFKB1–PROS1 axis in carcinogenesis of LGG