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Expression signature, prognosis value, and immune characteristics of Siglec-15 identified by pan-cancer analysis

ABSTRACT Sialic acid-binding immunoglobulin-like lectin 15 (Siglec-15) is considered a novel anti-tumor target comparable to programmed cell death 1 ligand 1(PD-L1). However, little is known about Siglec-15. Our study aims to understand its expression signature, prognosis value, immune infiltration pattern, and biological function using multi-omic bioinformatics from public databases and verify them in lung cancer patients. Integrated analysis of The Cancer Genome Atlas and Genotype-Tissue Expression portals showed Siglec-15 was overexpressed across cancers. Genetic and epigenetic alteration analysis was performed using cBioportal and UALCAN, showed Siglec-15 was regulated at the genetic and epigenetic levels. Survival estimated using Kaplan–Meier plotter indicated high Siglec-15 expression correlated with favorable or unfavorable outcomes depending on the different type and subtype of cancer. Components of immune microenvironment were analyzed using CIBERSORT, and the correlation between immune cells and Siglec-15 was found to be distinct across cancer types. Based on Gene Set Enrichment Analysis, Siglec-15 was implicated in pathways involved in immunity, metabolism, cancer, and infectious diseases. Lung cancer patients with positive Siglec-15 expression showed significantly short survival rates in progression-free survival concomitant with reduced infiltration of CD20 + B, and dendritic cells by immunohistochemistry. Quantitative real-time PCR results indicated the overexpression of Siglec-15 was correlated with activation of the chemokine signaling pathway. In conclusion, Siglec-15 could serve as a vital prognostic biomarker and play an immune-regulatory role in tumors. These results provide us with clues to better understand Siglec-15 from the perspective of bioinformatics and highlight the importance of Siglec-15 in many types of cancer.


Introduction
Tumor cells and the tumor microenvironment (TME) can secrete or express various signaling molecules, that act on immune checkpoints expressed in immune cells to suppress immune responses. The immune escape mechanisms in the TME are highly heterogeneous, and this inherent genomic instability helps cancer cells avoid cytotoxic or targeted therapy in metastatic tumors. 1 The approval of ipilimumab in 2011 as cancer immunotherapy has opened a new chapter in anticancer therapy. 2 Immunotherapy as a fourth method of cancer treatment after surgery, radiotherapy, and chemotherapy is turning the dream of a clinical cure for advanced tumor patients into reality. [3][4][5][6][7] Surprisingly, neoadjuvant immunotherapy appears to rewrite the current status of treatment for early cancer. 8 Blocking programmed cell death-1 (PD-1)/ PD-1 ligand 1 (PD-L1) is widely regarded as a successful strategy in normalization cancer immunotherapy. 9,10 However, treatment using anti-PD-1/PD-L1 pathway is not applicable to all patients. 11,12 Finding new targets to make up for the lack of PD-1/PD-L1 antibodies is imperative.
Siglecs are a family of sialic acid-binding immunoglobulinlike lectins that specifically recognize sialylated glycans and regulate immune cell function. 13 In recent years, an increasing number of Siglec members have been found to play a crucial role in tumor immunosuppression. [14][15][16] Siglec-7 and Siglec-9 expressed on the surface of natural killer (NK) cells and interacting with sialoglycans on cancer cells inhibited NK cells cytolytic capacity. 17,18 Siglec-9 on T cells and tumorassociated macrophages (TAMs) also play a role in "immune checkpoints" leading to immune evasion. 15 The glycoprotein CD24 is a novel "do not eat me" signal that prevents cancer cells from phagocytosis by binding to Siglec-10 on macrophages. 14 Siglec-15, originally classified into Siglecs family as a type I transmembrane protein. It is well conserved through vertebrate evolution and is mainly expressed on a subset of myeloid lineage cells. 19 Siglec-15 was found to be overexpressed in giant cell tumors of the bone and regulates bone remodeling and osteoclast differentiation through interaction with DAP12. [20][21][22][23][24] Siglec-15 expressed on TAMs may contribute to tumor immunosuppression by cooperating with DAP12 and Syk to increase TGF-β secretion. 25 Chen et al. first revealed that Siglec-15 showed high homology and similar domain composition with B7 family members, which have the ability to sustainably suppress T-cell responses. The expression of Siglec-15 is mutually exclusive to that of PD-L1. 26 Siglec-15 is considered a new promising target for immune normalization independent of the PD-1/PD-L1 pathway. Targeting Siglec-15 may be an effective alternative therapy for patients who do not respond to PD-1/PD-L1 antibodies. [26][27][28] A humanized monoclonal antibody against Siglec-15 is currently being evaluated in patients with advanced or metastatic solid tumors in phase I clinical trials. 29 This study was conducted to comprehensively analyze Siglec-15 mRNA expression signature, genetic and epigenetic characteristics, prognostic value, correlation with tumorinfiltrating immune cells, and associated pathways using nextgeneration sequencing (NGS) data from public platforms, 30 and we further performed immunohistochemistry (IHC) and real-time quantitative PCR (RT-qPCR) to verify its association with TME and signaling pathway. Our research aims to provide more information to better understand the significance of Siglec-15 in various cancers.

The cancer genome atlas (TCGA)
TCGA is a web-based, freely accessible database, which has generated large amounts of NGS data with a landscape of more than 11,000 tumors across 33 cancer types until 2018. It provided gene expression, methylation, copy number variation data sets, and clinical data. 30,31

Genotype-tissue expression (GTEx)
GTEx provides gene expression data from 53 normal tissue sites across nearly 1000 people by RNA sequencing, which is publicly available. RNA-seq data including GTEx and TCGA data were derived from UCSC Xena data hubs (http://xena. ucsc.edu/) for a pan-cancer differential expression of Siglec-15. TCGA and GTEx data were integrated using R version 3.5.3 and limma package.

cBioPortal
The cBioPortal for Cancer Genomics (http://www.cbioportal. org) is a repository of cancer genomics datasets. 32,33 We investigated the copy number alterations (CNA) and mutations of Siglec-15 in different cancers.

Kaplan-Meier plotter
Using the Kaplan-Meier plotter (http://kmplot.com/analysis/), 35 we analyzed the prognostic values of Siglec-15 mRNA expression in different cancers mainly from TCGA (RNA-seq) and Gene Expression Omnibus (GEO) (microarray). We computed the log-rank p-value and HR with 95% confidence intervals. A log p-value <0.05 was considered statistically significant. The significant survival data were collected and shown as forest plots created using R version 3.5.3 and survival package.

CIBERSORT
CIBERSORT calculates the putative proportion of immune cell fraction from gene expression profiles. 37 To estimate relative abundance of tumor-infiltrating immune cells in tumor mass from TCGA and GEO, we used a reference set with 22 sorted immune cell subtypes (LM22) by online analytical platform CIBERSORT (https://cibersort.stanford.edu/).

Gene set enrichment analysis (GSEA)
We downloaded the RNA-seq data from TCGA portal (https:// portal.gdc.cancer.gov/). We have chosen the Siglec-15 and its co-expression genes to perform GSEA to discover potential pathways by Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) terms affected by Siglec-15 using R version 3.5.3 and clusterProfiler, ggplot2, enrichplot, and org.Hs.eg.db packages. GO analysis was classified into three categories: biological process (BP), cellular component (CC), and molecular function (MF). Results with a normalized enrichment score (NES) ≥1.0 and false discovery rate (FDR) adjusted p-value <0.25 were considered statistically significant.

Patients and tissue samples
Data from 103 patients who had received radical (R0) resection with histological diagnosis of LUAD at the Tianjin Medical University Cancer Institute and Hospital, China, from January 2013 to May 2013, were retrospectively collected. All patients underwent R0 resection and lymph node dissection. The lymph node and lung cancer stages were categorized according to the eighth edition of the International Association for the Study of Lung Cancer TNM staging system. Histological analysis was reviewed by a pathologist after H&E staining. The 2 mm tissue cores are chosen as representative tumor parts on tissue microarray (TMA), obtained from nonnecrotic areas in the primary tumors using a needle. The use of patient information and tissues was sanctioned by the Ethics Committee of the Tianjin Medical University Cancer Institute and Hospital.

Immunohistochemical staining and evaluation
After deparaffinized, rehydrated, and antigen retrieval (Siglec-15, citrate buffer pH 6.0; CD4, CD8, CD20, CD11 c, FoxP3, CD68, CD163, EDTA 9.0), TMA slides were blocked with 3% hydrogen peroxide and 5% goat serum. After being incubated overnight at 4°C with primary antibodies, slides were incubated with enhancer and secondary antibody for half an hour at room temperature. A DAB Substrate Kit was used for chromogenic reaction. Finally, the sections were counterstained with hematoxylin, then dehydrated, cleared, and evaluated.

Statistical analysis
We summarized patient characteristics through descriptive statistics. Overall survival (OS) was defined as the time interval between the date of surgery and date of death. Progression-free survival (PFS) was defined as the time interval between the date of surgery and date of progression. Survival analyses were conducted by Kaplan-Meier curves (p-values from Log-rank test) by R version 3.5.3 and survival package. Hazard ratios (HR) were calculated using R package. All statistics in Tables 1-3 were twosided and analyzed through SPSS statistical software (version 24.0.0). Statistical significance was defined as p < .05.

RT-qPCR
Total RNA was isolated from patients with LUAD by Trizol and converted into cDNA by reverse transcription (GoScript™ Reverse Transcription System, USA). The mRNA expression of Siglec-15, immune-related genes, and pathway-related genes were assayed by RT-qPCR. Siglec-15 primers purchased from Sino Biological Inc. RT-qPCR primers of other genes are provided in Supplementary Table S1.

CNA and DNA methylation alterations of Siglec-15 across different human cancers
Genetic and epigenetic changes play a critical role in regulating cancer development and immune tolerance. Genetic alterations of Siglec-15 were further explored using cBioPortal. We found that patients with high Siglec-  Figure S3b). We then investigated the differential promoter DNA methylation status of Siglec-15 between cancer and adjacent normal tissues by using UALCAN (Figure 2d). Siglec-15 had lower DNA methylation levels in LUAD, CESC, COAD, KIRP, READ, and KIRC tissues than in adjacent normal tissues. Therefore, the abnormal increase of Siglec-15 mRNA expression in some cancers is likely a result of both genetic alterations and lower DNA methylations levels.

Correlation analysis between mRNA expression of Siglec-15 and prognostic value
After  Figure S4). In addition to TCGA, we analyzed ChIP and RNAseq data from GEO and other projects   Figure S5).

Relationship between mRNA expression of Siglec-15 and the tumor-immune microenvironment
After We also used multiple databases from GEO to analyze the relationship between Siglec-15 and the immune microenvironment for further validation (Figure 4b). Siglec-15 was positively associated with memory B cells and negatively macrophage (M0, M1) in BLCA; positively with γδT cells in BRCA-Basal; positively with macrophage (M0) in LUAD, which were consistent with the results in TCGA. We found that Siglec-15 was negatively associated with activated NK cells and positively associated with activated DCs and Tregs in BLCA in GSE31684. Siglec-15 was positively associated with naive B cells, neutrophils, and activated mast cells and negatively associated with follicular helper T cells in metastatic SKCM in GSE8401 compared to the associations in primary SKCM. Combining the results in TCGA and GSE30219 for LUAD and LUSC resulted a similar clustering pattern. This included a positive relationship with macrophages and a negative relationship with activated DCs. In BRCA-basal, Siglec-15 showed a significantly positive correlation with γδT cells in both TCGA and GSE21653. These results suggest opposite correlation patterns in esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) in GSE26886; however, the results were not significant. This is possibly owing to the  limited sample size (ESCC n = 9; EAC n = 21). Additional details can be found in Supplementary Table S2.

Preliminary experimental verification of Siglec-15 signature in LUAD
To verify the above results, we focused on Siglec-15 expression, prognostic value, relationship with immune cells, and associated pathways in LUAD and conducted in vitro experiment using tissues samples collected from the Tianjin Medical University Cancer Institute and Hospital. We first assessed the expression of Siglec-15 by 20 matched-pair LUAD tissue samples by RT-qPCR. The results showed Siglec-15 was significantly upregulated in primary tumor tissues (p = .0266) ( Figure 6a). As mentioned above, Siglec-15 was associated with activation of the "chemokine signaling pathway" and "cytokine-cytokine receptor interaction" in KEGG and GO terms "cytokine production" and "regulation of cytokine production" in LUAD (Figure 6b). We focused on the "chemokine signaling pathway" (Supplementary Figure S6a) to verify it by RT-qPCR using 20 LUAD tissue samples (Figure 6c). Expressions of genes belonging to chemokine signaling pathway (CXCR3, GNAI2, RAC2, and MMP9) and the downstream PI3 K-AKT signaling pathway (AKT3, LPAR6, and CDK6) showed a trend toward positive association with Siglec-15, although this was not significant. And PI3 K was significantly positively associated with Siglec-15 expression (Pearson r = 0.54, p = .01). We divided the cancer tissues into two groups (Siglec-15 high and Siglec-15 low) and evaluated mRNA expression levels of key genes in the pathways (Supplementary Figure S6b). PI3 K was highly expressed in the Siglec-15 high group (p < .05). Other genes showed a trend toward higher expression in Siglec-15 high group, although this was not significant.
Simultaneously, we performed IHC to evaluate the expression and prognosis value of Siglec-15 in a cohort of 103 LUAD specimens. The patient demographics and relationship between Siglec-15 expression and clinicopathological factors are shown in Table 1. Siglec-15 positive expression was not associated with tumor size, lymphatic metastasis, TNM stage, smoking index, age, and gender by the Fisher's exact test. A total of 17 (16.5%) patients had positive Siglec-15 staining and were classified as either membrane positive or membrane negative ( Table 1). Images of representative negative and positive samples were taken at 200× and 400× magnification (Figure 6d). T classification, N classification, and clinical stage were risk factors used to determine OS and PFS ( Table  2). The survival curves revealed that patients with positive expression of Siglec-15 had an unfavorable PFS (HR = 2.755, p = .0003). No significant difference in OS was observed between Siglec-15 positive and negative patients (HR = 1.734, p = .138) (Figure 6e).
Moreover, we investigated the prognostic value of Siglec-15 in selective patient subgroups of LUAD classified by clinicopathological factors (Table 3). Siglec-15 correlated with poor prognosis for PFS in patients with larger tumor size (T2+ T3 + T4), without lymph node metastasis (N0), earlier clinical stage (I+ II), and a smoking index less than 400. Positive Siglec-15 expression showed greater significant prognostic value in patients aged ≤60 y (PFS HR = 2.941, p = .003) than >60 y (PFS HR = 2.552, p = .034). Positive Siglec-15 expression indicated poorer PFS in male patients (HR = 5.04, p < .001) than in female patients (HR = 2.065, p = .043). No significant difference in OS was observed. We found that Siglec-15 was a poor prognostic predictor in our LUAD cohort, which is generally consistent with our survival analysis of the database, especially in GEO.
We perform IHC to evaluate the relative levels of immune cells infiltration of Siglec-15 in a cohort of 88 LUAD specimens with Siglec-15 negative and positive patients (Figure 6f, 6g,  Supplementary Figure S6). The patient clinicopathological characteristics and Siglec-15 expression are shown in Table  S4. The results showed that infiltration of B cells and DCs was stronger in Siglec-15 negative patients than in Siglec-15 positive patients (p < .05). However, there was no difference in the infiltration of macrophages and T cells (CD68 p = .36, CD163 p = .26, CD4 p = .11, CD8 p = .17, and FoxP3 p = .34). We found that Siglec-15 expression was negatively related to B cells and DCs, which is consistent with the results   from TCGA. However, Siglec-15 expression was not associated with macrophages and Tregs, which showed positive association with Siglec-15 in TCGA (Supplementary Figure S6d). Then, we further assessed the expression of these genes by RT-qPCR. Siglec-15 was positively associated with CD68 (r = 0.37, p = .11), CD163 (r = 0.50, p = .02), and FoxP3 (r = 0.58, p = .01) in 20 LUAD tissues.

Discussion
Tumor immunotherapy has greatly changed the paradigm in the management of tumor patients. Immune-checkpoint blockade therapy has improved survival in patients with advanced melanoma, non-small-cell lung cancer (NSCLC), and other cancers. 40 However, the anti-tumor effect of a singleagent PD-1 inhibitor is limited, with an effective response rate of less than 30%. 3,29,41,42 Chen et al. discovered a close relationship between Siglec-15 and the B7 family by protein sequence analysis. 26 Although several binding partners on tumor cells have been identified for Siglec-15, 43-45 the ligand on T cells for Siglec-15 is unclear. The role of suppressing T cells by Siglec-15 remains to be further investigated. Glycosylation pattern modification on tumor cells and bacteria to escape immunity has been known for decades; 46 however, little progress has been made in treatment strategies targeting glycosylation. Sialylation is one of the most common types of modifications in glycosylation. Siglecs expressed on tumor or immune cells bind to sialoglycans and play a differential immunoregulatory role. 47 In recent years, several studies have suggested that Siglecs are responsible for immune cell activation, proliferation, and apoptosis. Meanwhile, Siglecs have also been implicated in immune tolerance regulation and may play an essential role in autoimmune and autoinflammatory diseases and tumorigenesis. 16 Therefore, the Siglecs family of proteins have received increasing attention in tumor immunity. [14][15][16][17]26 An increasing number of Siglecs-or sialoglycan-targeted therapeutic drugs have been developed and may represent a potential novel immune-checkpoint. Targeting Siglec-15 may be an effective alternative therapy for patients that do not respond to PD-1/PD-L1 antibodies.
Genetic and epigenetic changes play a critical role in regulating cancer development and immune tolerance. For example, mutant PD-L1 with structural variations leads to aberrant PD-L1 expression and immunosuppression. 48,49 JAK2/PD-L1 /PD-L2 (9p24.1) amplifications can also bring about constitutive overexpression of PD-L1 and a significant response to checkpoint inhibitors. [50][51][52] Different mechanisms for regulating PD-L1 expression may reflect the varied roles on different cellular localization and cell types. Preliminary analysis suggested that Siglec-15 expression was genetically and epigenetically regulated through CNA and promoter methylation. In addition to a role in immune regulation, GSEA revealed that Siglec-15 was associated with oncogenic pathways (e.g. MAPK, PI3 K-Akt, Hippo, p53, and apoptosis). Whether Siglec-15 works as an oncogene and the exact mechanisms which regulated Siglec-15 remains to be elucidated.
Many of the genes are found to play different roles through different mechanisms in different cancers. [53][54][55][56] Similarly, Siglec-15 has variable prognostic significance in diverse cancers, and even in different subtypes of the same cancer. It is well accepted that tumor heterogeneity is a major challenge in cancer treatment. [57][58][59] Siglec-15 may play a role in inhibiting tumorigenesis and development in "cold tumors" like BRCA-luminal A/B. In "hot tumors" like BRCA-basal, Siglec-15 appears to exert an immunosuppressive function by regulating immune-related pathways. In some small sample phase II studies and retrospective studies, the single-agent efficacy of PD-1 or ipilimumab only helped individual patients with certain subtypes achieve tumor remission. 60 Siglec-15 overexpression in immune desert tumor, SARC, was involved in immune-related pathways and predicted poor prognosis. If the immune microenvironment can be converted from "cold" to "hot," anti-Siglec-15 monoclonal antibody may play a more critical role than PD-1/PD-L1 in SARC. In addition, Siglec-15 expression was the highest in the C6 (TGF-β dominant) immune subtype in LUAD, which has a worse prognosis among the six immune subtypes with the highest TGF-β signature. 39 It is worth noting that Siglec-15 associated pathways and functions in BLCA were totally different from other cancers. These phenomena may be attributed to inter-/intra-tumor heterogeneity and unique immunophenotyping, which presents new challenges for Siglec-15 studies.
Siglecs on macrophages and DCs could modulate toll-like receptor-induced cytokine responses. 45,61,62 The pathway analysis also suggested that Siglec15 was related to activation of the chemokine signaling pathway. CXC chemokine receptor CXCR3 is mainly expressed in Treg, B cells, and tumor cells. 63,64 There is an increasing trend for CXCR3 and MMP9 in patients with overexpressed Siglec-15, which may recruit Treg. 65 Our data analysis demonstrated that Siglec-15 was significantly associated with Tregs infiltration in cancers from TCGA and positively associated with FoxP3 in LUAD tissues by RT-qPCR. This may result in a specific Treg high Siglec15 high TME. These results indicate that Siglec-15 may exist in an "immune-excluded" environment, which is consistent with more Tregs and higher TGF-β signaling. 66 Therefore, application of anti-Siglec-15 antibody after other therapeutic interventions may hold promise.
There were some differences between the public data analysis and experimental verification by IHC and RT-qPCR. This phenomenon may be due to that we currently focused on Siglec-15 expression on the membrane of tumor cells and used tissue microarray in the assessment of the complex TME. Firstly, Siglec-15 is primarily expressed on macrophage and tumor cells. We cannot exclude the simultaneous expression Siglec-15 in the membrane and cytoplasm. Differences in Siglec-15 expression in macrophage and tumor cells remain unclear. Secondly, TME is widely known for its complexity and variability caused by spatial and temporal heterogeneity. 57 In addition to the number of immune cells, the distribution of immune cells in the central core, tumor stroma, and peripheral areas 57 contributes to different immune infiltration patterns in the same tissue. Thirdly, the number of patients in our validation cohort was limited to 103 patients. Recurrent patients received different treatment options, which may explain this phenomenon. In one word, more samples and more detailed analysis in specific subgroups are needed to conduct a more accurate and robust evaluation. In addition, our results are based on NGS analysis, which are derived from bulk cells and limited our analysis on accurate differentiation of immune cells and tumor cells. Therefore, single-cell RNA sequencing of sorted immune cells are needed to provide more exact information. Although the experimental results in LUAD samples were generally consistent with data analysis in TCGA and GEO, additional experiments are required to confirm the significance of Siglec-15 in other cancers.
In summary, we investigated Siglec-15 mRNA expression characteristics, prognostic value landscape, relationship with tumor-infiltrating immune cells, and associated pathways in various cancers from the perspective of multi-omic bioinformatics ( Figure 7). We further verified that Siglec-15 expression was associated with poor prognosis and an inhibitory microenvironment in patients with LUAD. These results validated the importance of Siglec-15 expression in cancer prognosis and treatment and identified significant areas for further exploration and confirmation.