HMMR  is a prognostic-related biomarker of p ancreatic ductal adenocarcinoma  and promotes the development of  p ancreatic ductal adenocarcinoma

Abstract

Background HMMR is an essential oncogene, which is highly expressed in most tumors and is related to the survival and prognosis of many tumors. Pancreatic adenocarcinoma (PAAD) is a common malignant tumor of the digestive tract. The five-years overall survival (OS) is less than 10%. This study aim to investigate the relationship between HMMR expression and survival prognosis in patients with different tumors, the function of HMMR in pancreatic ductal adenocarinoma (PDAC) cells and explore the possible mechanisms in tumorigensis and development.

Methods The Oncomine, Tumor Immunoassay Resource (TIMER) and GEPIA databases were used for assessing the expression of HMMR, after which PrognoScan database and Kaplan-Meier plotter database was used to explore the relationship between HMMR and tumour outcomes including overall survival (OS), diseases free survival (DFS) and relapse free survival (RFS). Then, we investigated the function of HMMR in PDAC cells. Finally, we conduct HMMR-related gene enrichment analysis.

Results We determined HMMR expression to be significantly correlated with outcome in multiple types of cancer in the Cancer Genome Atlas (TCGA), with the effect being particularly pronounced in pancreatic ductal adenocarinoma. Elevated HMMR expression was found to be significantly correlated with PDAC staging and grade. Furthermore, we found that HMMR promotes the proliferation, invasion and migration of PDAC cells. The KEGG data suggest that “Oocyte meiosis”, “Cell cycle”, “Progesterone-mediated oocyte maturation”, “FoxO signaling pathway” and “p53 signaling pathway” might be involved in the effect of HMMR on tumor pathogenesis.

Conclusions HMMR is high expression and associated with patient outcome in multiple cancer types, in addition, HMMR is a key factor which governs invasion and migration to PDAC, potentially playing a vital role in governing tumor invasion and migration and thus representing a valuable prognostic biomarker in PDAC patients.

1 Background

Pancreatic cancer (PC) is still one of the deadliest malignant diseases in the world[1]. In the United States, PC is the third leading cause of cancer death, and the 5-year survival rate is less than 10%, with only marginal improvement during the last decades[2]. Pancreatic ductal adenocarcinoma (PDAC) accounts for more than 90% of all PC and is therefore the major contributor to the aforementioned mortality[3]. The incidence and mortality of pancreatic cancer has been increasing year by year worldwide, and 80% of patients have been found to have local metastasis when diagnosed[4]. Surgery is the main treatment for pancreatic cancer; however, 80% of patients are not suitable for surgery, and the recurrence rate among patients who have undergone resection is very high. Further, there are numerous postoperative complications, all of which result in unsatisfying surgical outcomes[5]. In recent years, research on molecular diagnosis and targeted biological therapy of disease has seen certain progress. Diagnosis and therapy have gradually become important means to improve the prognoses of patients with malignant tumors. Thanks to the rapid development of genomics, tumor-related genes are continuously being discovered. A number of research studies have shown that genes play vital roles in the incidence and development of pancreatic cancer[5].

Hyaluronan mediated motility receptor (HMMR, also known as RHAMM, XRHAMM, IHABP, or CD168) gene, was first identified as a constituent of a novel hyaluronan receptor complex purified from the supernatants of murine cells[12], which was originally detected in human and rats, map to Human Chromosome 5q33.2–qter and Mouse Chromosome 11[12]. Many studies have found that HMMR is highly expressed in various malignant tumors, including bladder cancer[14], pancreatic cancer[15, 16], glioma[17], gastric cancer[18], colorectal cancer[19] and so on. And some studies showed that a high expression of HMMR is associated with worse prognosis as it promotes cancer growth and metastasis. Previous studies have shown that short peptides of HMMR can be effectively presented by DC cells and activate T cell immunity[20-23]. Although some researchers found that HMMR was overexpressed in various cancer types, and considered as a tumor-associated antigen and therapeutic target of immunotherapy. However, there is still no pan-cancer evidence on the relationship between HMMR and various tumor types based on big clinical data, and the mechanisms of HMMR governs tumour progression in pancreatic ductal adenocarinoma remain unclear.

Herein, we conducted a comprehensive assessment of the relationship between HMMR and patient prognosis using databases including Oncomine, PrognoScan and Kaplan-Meier plotter. We further investigated the function of HMMR in pancreatic ductal adenocarcinoma cells and the potential molecular mechanism of HMMR in the pathogenesis or clinical prognosis of different cancers. Our results offer novel insights into the functional role of HMMR in pancreatic ductal adenocarcinoma cells, and may act as a prognostic-related biomarker of pancreatic ductal adenocarcinoma.

2 Materials And Methods

2.1 Oncomine database analysis

The Oncomine database compiled 86,733 samples and 715 gene expression data sets into a single comprehensive database designed to facilitate data mining efforts[24]. We therefore used this database to assess the association between HMMR expression and prognostic outcome in various tumour types (https://www.oncomine.org/resource/login.html).

2.2 PrognoScan database analysis

The PrognoScan database is designed to facilitate meta-analyses of gene prognostic value by comparing the relationship between gene expression and relevant outcome including overall survival (OS) in a wide range of published cancer microarray data sets[25]. We therefore used this database to assess the relationship between HMMR expression and patient outcome (http://www.abren.net/Progn oScan/).

2.3 TIMER2 database analysis

We input HMMR in the “Gene_DE” module of TIMER2 (tumor immune estimation resource, version 2) web (http://timer.cistrome.org/) and observed the expression difference of HMMR between tumor and adjacent normal tissues for the different tumors or specific tumor subtypes of the TCGA project. Moreover, we used the “Gene_Corr” module of TIMER2 to supply the heatmap data of the selected genes, which contains the partial correlation (cor) and P-value in the purity-adjusted Spearman’s rank correlation test.

2.4 GEPIA2 database analysis

GEPIA2 is an online database which facilitates the standardized analysis of RNA-seq data from 9,736 tumour samples and 8,587 normal control samples in the TCGA and GTEx data sets (http://gepia.cancer-pku.cn/index.html)[25]. To further assess the difference in HMMR expression levels in those tumour types which did not have normal tissues in TIMER2 database, we use the GEPIA2 database to analysis them. We also employed this database to assess the link between HMMR expression and patient prognosis in multiple tumour types.

2.5 Kaplan-Meier plotter analysis

The Kaplan-Meier plotter offers a means of readily exploring the impact of a wide array of genes on patient survival in 21 different types of cancer, with large sample sizes for the breast (n = 6,234), ovarian (n = 2,190), lung (n = 3,452) and gastric (n = 1,440) cancer cohorts[27]. We therefore used this database to explore the association between HMMR expression and outcome in patients with gastric, breast, ovarian, lung cancer and pancreatic ductal adenocarinoma, analysing the impact of both clinicopathological factors and HMMR on patient outcome in pancreatic ductal adenocarinoma patients (http://kmplot.com/analysis/).

2.6 Genetic alteration analysis

Firstly, we logging into the cBioPortal web (https://www.cbioportal.org/)[28, 29], and secondly chose the “TCGA Pan Cancer Atlas Studies” in the “Quick select” section and entered “HMMR” for queries of the genetic alteration characteristics of HMMR. The results of the alteration frequency, mutation type and CNA (Copy number alteration ) across all TCGA tumors were observed in the “Cancer Types Summary” module. The mutated site information of HMMR can be displayed in the schematic diagram of the protein structure or the 3D (Three-dimensional) structure via the “Mutations” module.

2.7 Gene function analysis

We select human pancreatic ductal adenocarcinoma (PDAC) cell lines for functional analysis of HMMR. The cell lines HPDE6-C7, PANC-1, Capan-2, SW1990 and BxPC-3 were cultured in RPMI 1640 medium containing 10% fetal bovine serum, which was placed in incubator at 37 ℃ in a humidified atmosphere with 5% CO2. After 2 to 3 days of cell passage, the cells in the logarithmic growth phase were selected for further experiment. MTS assay. Dispense 5 × 10³ per cells into wells of the 96-well plates in a final volume of 100 µl. Incubate the plate at 37 ℃ for 48 - 72 hours in a humidified, 5% CO2 atmosphere. Add 20 µl per well of CellTiter 96® AQueous One Solution Reagent. Incubate the plate at 37 ℃ for 1 - 4 hours in a humidified, 5% CO2 atmosphere. Record the absorbance at 490nm using a 96-well plate reader. Each experiment was conducted with 3 replicates[30]. For colony formation assays, 500 cells were seeded per well in 6-well plates and cultured for 2 weeks. The colonies were fixed with 4% paraformaldehyde and stained with 1% crystal violet. Only colonies containing more than 50 cells were counted. For wound healing assay, 5 × 10⁵ cells were seeded into 6-well plates and grown to confluence. Mitomycin C (10 µg/mL) was used to suppress cell proliferation before scratching[31]. Wounds were created by scraping the confluent cell monolayers with a 10 µl pipette tip. After extensively rinsed to remove cellular debris, cells were cultured in serum-free medium. Observe wound closure rate after incubating for 24 h, and images were taken using an inverted microscope TE-2000S (Nikon, Tokyo, Japan). Transwell invasion assay was performed in a 24-well transwell plate with 8-µm polyethylene terephthalate membrane fiters (Corning Costar Corp, Corning, NY). 1 × 10⁵ cells in 200 µl of serum-free medium were added to the upper chambers, which contained matrigelcoated membranes (BD Biosciences). Each lower chamber was filed with 500 µl medium with 10% FBS. After 24 h of incubation, cells that invaded to the bottom chamber were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. Invasive cells were counted in five randomly chosen fields (magnification, × 200) per well.

Further details of materials and methods are described in the Supporting Materials and Methods.

2.8 HMMR-related gene enrichment analysis

We first searched the STRING website (https://string-db.org/) using the query of a single protein name (“HMMR”) and organism (“Homo sapiens”). Subsequently, we set the following main parameters: minimum required interaction score [“Low confidence (0.150)”], meaning of network edges (“evidence”), max number of interactors to show (“no more than 50 interactors” in 1st shell) and active interaction sources (“experiments”). Finally, the available experimentally determined HMMR-binding proteins were obtained. We used the “Similar Gene Detection” module of GEPIA2 to obtain the top 100 HMMR-correlated targeting genes based on the datasets of all TCGA tumor and normal tissues. We also applied the “correlation analysis” module of GEPIA2 to perform a pairwise gene Pearson correlation analysis of HMMR and selected genes. The log2 TPM was applied for the dot plot. The P-value and the correlation coefficient (R) were indicated. Moreover, we used the “Gene_Corr” module of TIMER2 to supply the heatmap data of the selected genes, which contains the partial correlation (cor) and P-value in the purity-adjusted Spearman’s rank correlation test.

We used Jvenn, an interactive Venn diagram viewer[32], to conduct an intersection analysis to compare the HMMR-binding and interacted genes. Moreover, we combined the two sets of data to perform KEGG (Kyoto encyclopedia of genes and genomes) pathway analysis. In brief, we uploaded the gene lists to DAVID (Database for annotation, visualization, and integrated discovery) with the settings of selected identifier (“OFFICIAL_GENE_SYMBOL”) and species (“Homo sapiens”) and obtained the data of the functional annotation chart. The enriched pathways were finally visualized with the “dplyr” and “ggplot2” R packages. In addition, we applied the "clusterProfiler" R package to conduct GO (Gene ontology) enrichment analysis. The data for BP (Biological process), CC (Cellular component), and MF (Molecular function) were visualized as cnetplots, using the cnetplot function (circular = F, colorEdge = T, node_label = T). The R language software [R-4.04, 64-bit] (https://www.r-project.org/) was used in this analysis. Two-tailed P <0.05 was considered statistically significant.

3 Results

3.1 Assessment of HMMR expression in different cancer and normal tissues

We first assessed the expression of HMMR in multiple tumour and normal tissue types using the Oncomine database, revealing that expression of this gene was elevated relative to normal tissue controls for bladder, brain, breast, cervical, rectal, colorectal, oesophageal, gastric, head and neck, renal, liver, lung, lymphoma, ovarian, pancreatic, sarcoma and prostate cancers. We also found that relative to normal tissue controls, HMMR expression was lower in leukemia and other cancer tissues (Figure 1A). Detailed findings in particular tumour types are compiled in Table S1. We also used the TCGA and TIMER databases to assess how HMMR expression differs in particular tumour types. We found that the expression of HMMR was significantly elevated relative to normal controls in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA) and uterine corpus endometrial carcinoma (UCEC). Differences between the expression of HMMR in tumours and normal adjacent tissue samples in the TCGA data set are shown in Figure 1B.

To further evaluated the expression difference of HMMR between the normal tissues and tumor tissues, we use the GTEx dataset as controls, which shown that high HMMR expression in tumor than the normal tissue in adrenocortical carcinoma (ACC), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD) (Figure 1C, P < 0.01), and sarcoma (SARC), thymoma (THYM), uterine carcinosarcoma (UCS), (Figure S1A-C, P < 0.01). However, we did not obtain a significant difference in kidney chromophobe (KICH), brain lower grade glioma (LGG) and pheochromocytoma and paraganglioma (PCPG), as shown in Figure S1D-F. What more, we found the expression of HMMR was high in normal tissues than tumor tissues in acute myeloid leukemia (LAML) and testicular germ cell tumors (TGCT) are shown in Figure S1G, H.

3.2 The association between HMMR expression and cancer patient prognosis

We next explored the link between the expression of HMMR and cancer patient outcome using the PrognoScan database (Table1 and Tables S2–S4). We found that multiple cancer types exhibited a significant association between patient prognosis and HMMR expression including bladder, brain, blood, breast, colon, ovarian skin and lung cancer (Figure 2A–H and Figure S2). We additionally employed the Kaplan-Meier plotter database in order to assess how HMMR expression relates to prognosis in a range of cancer types, revealing its elevation to be significantly linked with a poorer prognosis in ovarian cancer (OS HR = 1.32, 95% CI = 1.15–1.5, P = 4e-5; PFS HR = 1.3, 95% CI = 1.14–1.48, P = 7e-5), lung cancer (OS HR = 1.7, 95% CI = 1.49–1.93, P = 4e-16; PFS HR = 1.7, 95% CI = 1.67–2.47, P = 6.5e-13) and pancreatic ductal adenocarcinoma (PAAD) (OS HR = 2.31, 95% CI = 1.51–3.55, P = 8e-5; RFS HR = 3.62, 95% CI = 1.4–9.41, P = 0.0048) (Figure 2I–L and Table 2). However, we found reduced HMMR expression to be correlated with poorer patient prognosis in gastric cancer (OS HR = 0.59, 95% CI = 0.48–0.71, P = 4.7e-8; PFS HR = 0.63, 95% CI = 0.5–0.8, P = 8.2e-5) (Figure 2M–N). There was not any significant relationship between the expression of HMMR and the OS prognosis of breast cancer patients (Figure 2O), but the high HMMR expression level have a significant relationship with the RFS prognosis of breast cancer patients (RFS HR = 1.32, 95% CI = 1.19–1.46, P = 8.1e-8) (Figure 2P). In addition, we found HMMR expression to be linked with poor pancreatic ductal adenocarcinoma patients prognosis, by use the Kaplan-Meier plotter database to assess the relationship between HMMR expression and patient clinicopathological findings. We found that HMMR expression correlated significantly with OS, DFS and patient gender, stage, grade of pancreatic ductal adenocarcinoma patients (Table 2). We further used the GEPIA2 database to assess how HMMR expression relates to patient prognosis, analysing 33 TCGA cancer types and revealing that HMMR expression correlated both with OS and DFS in ACC, BLCA, KIRP, LGG, LIHC, LUAD and PAAD (Figure S3 and Figure S4).

We also conducted a analyses by Sangerbox tool to show the correlation between high expression HMMR and poor OS for different tumors, as shown in (Figure S5), high HMMR expression was associated with poor OS for LUAD, UCEC, BLCA, PAAD, KIRP, LIHC, MESO, KIRC, HNSC, LGG, KICH, ACC and UVM (all P < 0.05). The above results clearly demonstrate that HMMR expression significantly correlated with poorer outcome in multiple tumour types.

3.3 Genetic alteration analysis of HMMR

We observed the genetic alteration status of HMMR in different tumor samples of the TCGA cohorts. As shown in (Figure 3A), the highest alteration frequency of HMMR (> 6%) appears for patients with UCEC with “mutation” as the primary type. The “amplification” type of CNA was the primary type in the KIRC and CHOL cases, which show an alteration frequency of > 6% and ~3%, respectively (Figure 3A). The types, sites and case number of the HMMR genetic alteration are further presented in (Figure 3B). We found that missense mutation of HMMR in the Q52L site was detected in 1 cases of PAAD (Figure 3B), is able to induce a frame shift mutation of the HMMR gene. We can observe the Q52L site in the 3D structure of HMMR protein (Figure 3C). In addition, we analyzed the correlation between HMMR expression and TMB (Tumor mutational burden)/MSI (Microsatellite instability) across all tumors of TCGA. As shown in (Figure S6), we observed a positive correlation for GBM (P = 1.7e-06), LUAD (P = 0.00092), PRAD (P = 3.4e-29), UCEC (P = 0.0074), COAD (P = 0.013), STAD (P = 2e-04), SKCM (P = 4.5e-07), KIRC (P = 0.003), HNSC (P = 0.042), LAML (P = 0.029), KICH (P = 4.3e-08), and ACC (P = 0.0039). HMMR expression is negatively correlated with MSI of DLBC (P = 4.4e-05) but is positively correlated with that of GBM (P = 5.9e-05), PRAD (P = 0.032), UCEC (P = 8.8e-05), SARC (Sarcoma) (P = 1e-07), COAD (P = 0.00091), and STAD (P = 2.2e-05) (Figure S7). This result deserves more in-depth research.

3.4 Gene function analysis

We select human pancreatic ductal adenocarcinoma (PDAC) cell lines for functional analysis of HMMR. The cell lines HPDE6-C7, PANC-1, Capan-2, SW1990 and BxPC-3 were cultured in RPMI 1640 medium containing 10% fetal bovine serum, which was placed in incubator at 37 ℃ in a humidified atmosphere with 5% CO₂. After 2 to 3 days of cell passage, the cells in the logarithmic growth phase were selected for further experiment.

To study expression of HMMR in PDAC cells, real-time polymerase chain reaction (PCR) and western blotting were performed. Compared with HPDE6-C7 cells, which are immortalized human normal pancreatic ductal epithelium cells, HMMR messenger RNA (mRNA) and protein were highly expressed in PDAC cells (Figure 4A and Figure 4B). Notably, HMMR expression in high-metastasis potential cell lines, such as Capan-2 (Figure 4A and Figure 4B).

To understand the function of HMMR in PDAC cells, we manipulated HMMR expression in Caoan-2 by short hairpin RNA (shRNA) knockdown. Three shRNA (shRNA1, shRNA2, and shRNA3) were designed to silence HMMR expression in Capan-2 cells named as Capan-2-shHMMR subsequently. Expression level of HMMR was identified by real-time PCR and western blotting; shRNA2 was the most effective one and was chosen for further study (Figure S8A, B). Compared to Capan-2, Capan-2-shHMMR cells had a lower absorbance in methyl thiazol tetrazolium assay, which indicated a lower proliferation rate (Figure S8C). Consistently, Capan-2 cells also formed more colonies compared to Capan-2-shHMMR in colony formation assay (Figure 4C). .

The wound-healing and transwell assays were used to investigate migration and invasion capacity. Results showed that Capan-2-control cells had a faster wound closure rate and more invasion cells than Capan-2-shHMMR cells (Figure 4D, 4E). It suggests that HMMR promotes PDAC cells proliferation, migration, and invasion capacity in vitro.

3.5 Enrichment analysis of HMMR-related partners

To further investigate the molecular mechanism of the HMMR gene in tumorigenesis and development, we attempted to screen out the targeting HMMR-binding proteins and the HMMR expression-correlated genes for a series of pathway enrichment analyses. Based on the STRING tool, we obtained a total of 29 HMMR-binding proteins, which were supported by experimental evidence. Figure 5A shows the interaction network of these proteins. We used the GEPIA2 tool to combine all tumor expression data of TCGA and obtained the top 100 genes that correlated with HMMR expression. As shown in Figure 5B, the HMMR expression level was positively correlated with that of KIF11 (Kinesin Family Member 11) (R = 0.77), BUB1 (Budding Uninhibited By Benzimidazoles 1) (R = 0.77), CCNA2 (Cyclin A2) (R = 0.77), AURKA (Aurora Kinase A) (R = 0.70), BRCA1 (Breast Cancer 1) (R = 0.63), and TPX2 (Targeting Protein For Xklp2) (R = 0.73) genes (all P < 0.001). The corresponding heatmap data also showed a positive correlation between HMMR and the above six genes in the majority of detailed cancer types (Figure 5C). An intersection analysis of the above two groups showed three common member, namely, AURKA, BRCA1 and TPX2 (Figure 5D).

We combined the two datasets to perform KEGG and GO enrichment analyses. The KEGG data of Figure 6A suggest that “Oocyte meiosis”, “Cell cycle”, “Progesterone-mediated oocyte maturation”, “FoxO signaling pathway” and “p53 signaling pathway” might be involved in the effect of HMMR on tumor pathogenesis. The GO enrichment analysis data further indicated that most of these genes are linked to the pathways or cellular biology of microtubule motor activity, such as microtubule binging, microtubule cytoskeleton organization, and protein kinase regulator activity, such as protein serine/threinine kinase activator activity, protein serine/threinine kinase activity and cyclin-dependent protein kinase holoenzyme complex, and others. (Figure 6B and Figure S9).

4 Discussion

HMMR is a receptor for hyaluronic acid (HA), which is ubiquitous component of the extracellular matrix (ECM) and is highly expressed in the tumor microenvironment[33-35]. When hyaluronan binds to HMMR, the phosphorylation of a number of proteins, including PTK2/FAK1 occurs. HMMR playing broad regulatory roles and controlling key physiological processes including cell motility, transformation and metastasis formation, via regulatory extracellular-regulated kinase (ERK) activity, HMMR also be involved in signal transduction via Src tyrosine kinase family, expressed in the cytoplasm, nucleus and cell surface, five isoforms (58, 60, 64, 70, 84kDa), regulating cell motility and cell cycle. Many study have shown that HMMR can be used as a prognostic marker in some cancer patients, such as hepatocellular carcinoma[36, 37], head and neck squamous cell carcinomas[38], colorectal cancer[38], esophageal adenocarcinoma[40], lung adenocarcinoma[41], renal cell carcinoma[42] etc. However, no studies have investigated HMMR as a prognostic marker in pancreatic cancer, so this study to investigate the the relationship between HMMR and different tumors, the functions in PDAC cells and the feasibility of HMMR as a biomarker for survival and prognosis of pancreatic cancer patients.

In this report, we assessed the expression of HMMR in 33 different types of cancers using the independent Oncomine, TIMER2 and GEPIA databases, revealing clear differences between tumour and normal tissue expression of HMMR in many cancers. Oncomine data revealed elevated HMMR levels in bladder, brain, breast, cervical, rectal, colorectal, oesophageal, gastric, head and neck, renal, liver, lung, lymphoma, ovarian, pancreatic, sarcoma and prostate cancers. We also found that relative to normal tissue controls, HMMR expression was lower in leukemia and other cancer tissues (Figure 1A). TCGA dataset analysis indicated that there was elevated HMMR expression in BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, READ, STAD, THCA and UCEC relative to adjacent controls (Figure 1B). GEPIA dataset analysis showed that elevated HMMR levels in ACC, DLBC, OV and PAAD patients (Figure 1C). Altered HMMR expression in a range of different cancers may be due to the different means of data collection in different studies, or it may relate to differences in the underlying biological mechanisms. Across GAPIE and Kaplan-Meier plotter databases, we consistently observed a correlation between elevated HMMR expression and a poor PAAD prognosis (Figure S3J, Figure S4J and Table 2). These results together thus suggest that HMMR may have value as a PAAD prognostic biomarker.

In this study, we first presented evidence of the potential correlation between HMMR expression and MSI or TMB across all TCGA tumors. Furthermore, we integrated the information on HMMR-binding components and HMMR expression-related genes across all tumors for a series of enrichment analyses and identified the potential impact in the etiology or pathogenesis of cancers.

An additional key finding in this study is that the expression of HMMR correlated with the proliferation, invasion and migration of pancreatic cancer cells. We found that HMMR expression was increase remarkably in Capan-2 compare with HPDE6-C7 cells (Figure 4A and Figure 4B). We further found compared to Capan-2, Capan-2-shHMMR cells had a lower absorbance in methyl thiazol tetrazolium assay and lesser colonies (Figure S8C and Figure 4C). The wound-healing and transwell assays results showed that Capan-2-control cells had a faster wound closure rate and more invasion cells than Capan-2-shHMMR cells (Figure 4D, 4E). All of these results suggests that HMMR promotes PDAC cells proliferation, migration, and invasion capacity in vitro.

It was reported that HMMR as a homeostasis, mitosis, and meiosis regulator rather than a hyaluronan receptor[43]. Some research showed that HMMR regulates the proliferation of certain stem cell populations and may enable certain hallmarks of cancer[44, 45], and the expression of HMMR is cell cycle-regulated with peak expression between late G2 phase and early mitosis[46]. HMMR is a largely coiled-coil protein that can bind to microtubules directly through its N-terminus[47] and localize to the centrosome through a C-terminal bZip motif[48], which is structurally very similar to the C-terminal bZip motif in Xklp2 that enables an interaction with TPX2[48, 49]. The similarity in these domain structures seeded the hypothesis that HMMR also interacts with TPX2, which has been confirmed experimentally through the study of HMMR/RHAMM in human mitotic cells[48, 50, 51]. The interaction between HMMR and TPX2 is important for Ran-dependent microtubule assembly near chromosomes[52, 53]. HMMR was predicted to be regulated by proliferation associated transcription factors, FOXM1, E2F4, and MYC[52, 53] and, experimentally, by YAP-TEAD of the Hippo pathway[55]. It also be reported that HMMR expression is transcriptionally downregulated by the tumor suppressor TP53[56]. In this study, we analyzed the biological functions and signaling pathways of HMMR by KEGG analysis and GO enrichment, and the results were consistent with those reported by other scholars (Figure 6 and Figure S9).

Conclusion

In this study, we first pan-cancer analyses of HMMR indicated statistical correlations of HMMR expression with clinical prognosis, tumor mutational burden or microsatellite instability across multiple tumors. HMMR may be an important oncogene regulator of cells proliferation, invasion and migration and a valuable prognostic biomarker in pancreatic cancer patients.

Declarations

Acknowledgements

Not applicable.

Funding

Funding information is not applicable.

Authors’ Contributions

NT and GDL conceived the project and wrote the manuscript. XLD, XY and YXZ participated in data analysis. XLD and XY participated in discussion and language editing. NT and GDL reviewed the manuscript.

Ethics declaration

Ethics approval and consent to participate

Not applicable

Patient consent for publication

Identifying information, including names, initials, data of birth or hospital numbers, images or statements are not included in the manuscript.

Publication of clinical datasets

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1. Kamisawa T, Wood LD, Itoi T, Takaori K: Pancreatic cancer. LANCET 2016, 388(10039):73-85.
2. Siegel RL, Miller KD, Jemal A: Cancer statistics, 2019. CA Cancer J Clin 2019, 69(1):7-34.
3. Muniraj T, Jamidar PA, Aslanian HR: Pancreatic cancer: a comprehensive review and update. Dis Mon 2013, 59(11):368-402.
4. Matsubayashi H, Takaori K, Morizane C, Maguchi H, Mizuma M, Takahashi H, Wada K, Hosoi H, Yachida S, Suzuki M et al: Familial pancreatic cancer: Concept, management and issues. World J Gastroenterol 2017, 23(6):935-948.
5. Di Marco M, Grassi E, Durante S, Vecchiarelli S, Palloni A, Macchini M, Casadei R, Ricci C, Panzacchi R, Santini D et al: State of the art biological therapies in pancreatic cancer. World J Gastrointest Oncol 2016, 8(1):55-66.
6. Duan L, Hu XQ, Feng DY, Lei SY, Hu GH: GPC-1 may serve as a predictor of perineural invasion and a prognosticator of survival in pancreatic cancer. ASIAN J SURG 2013, 36(1):7-12.
7. Yi XP, Han T, Li YX, Long XY, Li WZ: Simultaneous silencing of XIAP and survivin causes partial mesenchymal-epithelial transition of human pancreatic cancer cells via the PTEN/PI3K/Akt pathway. MOL MED REP 2015, 12(1):601-608.
8. Shi S, Cao H: Shikonin promotes autophagy in BXPC-3 human pancreatic cancer cells through the PI3K/Akt signaling pathway. ONCOL LETT 2014, 8(3):1087-1089.
9. Yang Z, Li D, Liu Z, Miao X, Yang L, Zou Q, Yuan Y: BIRC7 and KLF4 expression in benign and malignant lesions of pancreas and their clinicopathological significance. CANCER BIOMARK 2016, 17(4):437-444.
10. Wang L, Xiong L, Wu Z, Miao X, Liu Z, Li D, Zou Q, Yang Z: Expression of UGP2 and CFL1 expression levels in benign and malignant pancreatic lesions and their clinicopathological significance. WORLD J SURG ONCOL 2018, 16(1):11.
11. Ke MJ, Ji LD, Li YX: Bioinformatics analysis combined with experiments to explore potential prognostic factors for pancreatic cancer. CANCER CELL INT 2020, 20:382.
12. Hardwick C, Hoare K, Owens R, Hohn HP, Hook M, Moore D, Cripps V, Austen L, Nance DM, Turley EA: Molecular cloning of a novel hyaluronan receptor that mediates tumor cell motility. J CELL BIOL 1992, 117(6):1343-1350.
13. Spicer AP, Roller ML, Camper SA, McPherson JD, Wasmuth JJ, Hakim S, Wang C, Turley EA, McDonald JA: The human and mouse receptors for hyaluronan-mediated motility, RHAMM, genes (HMMR) map to human chromosome 5q33.2-qter and mouse chromosome 11. GENOMICS 1995, 30(1):115-117.
14. Niedworok C, Kretschmer I, Rock K, Vom DF, Szarvas T, Hess J, Freudenberger T, Melchior-Becker A, Rubben H, Fischer JW: The impact of the receptor of hyaluronan-mediated motility (RHAMM) on human urothelial transitional cell cancer of the bladder. PLOS ONE 2013, 8(9):e75681.
15. Cheng XB, Sato N, Kohi S, Koga A, Hirata K: Receptor for Hyaluronic Acid-Mediated Motility is Associated with Poor Survival in Pancreatic Ductal Adenocarcinoma. J CANCER 2015, 6(11):1093-1098.
16. Choi S, Wang D, Chen X, Tang LH, Verma A, Chen Z, Kim BJ, Selesner L, Robzyk K, Zhang G et al: Function and clinical relevance of RHAMM isoforms in pancreatic tumor progression. MOL CANCER 2019, 18(1):92.
17. Tilghman J, Wu H, Sang Y, Shi X, Guerrero-Cazares H, Quinones-Hinojosa A, Eberhart CG, Laterra J, Ying M: HMMR maintains the stemness and tumorigenicity of glioblastoma stem-like cells. CANCER RES 2014, 74(11):3168-3179.
18. Zhang H, Ren L, Ding Y, Li F, Chen X, Ouyang Y, Zhang Y, Zhang D: Hyaluronan-mediated motility receptor confers resistance to chemotherapy via TGFbeta/Smad2-induced epithelial-mesenchymal transition in gastric cancer. FASEB J 2019, 33(5):6365-6377.
19. Zlobec I, Terracciano L, Tornillo L, Gunthert U, Vuong T, Jass JR, Lugli A: Role of RHAMM within the hierarchy of well-established prognostic factors in colorectal cancer. GUT 2008, 57(10):1413-1419.
20. Greiner J, Schmitt A, Giannopoulos K, Rojewski MT, Gotz M, Funk I, Ringhoffer M, Bunjes D, Hofmann S, Ritter G et al: High-dose RHAMM-R3 peptide vaccination for patients with acute myeloid leukemia, myelodysplastic syndrome and multiple myeloma. HAEMATOLOGICA 2010, 95(7):1191-1197.
21. Schmitt A, Barth TF, Beyer E, Borchert F, Rojewski M, Chen J, Guillaume P, Gronau S, Greiner J, Moller P et al: The tumor antigens RHAMM and G250/CAIX are expressed in head and neck squamous cell carcinomas and elicit specific CD8+ T cell responses. INT J ONCOL 2009, 34(3):629-639.
22. Willemen Y, Van den Bergh JM, Bonte SM, Anguille S, Heirman C, Stein BM, Goossens H, Kerre T, Thielemans K, Peeters M et al: The tumor-associated antigen RHAMM (HMMR/CD168) is expressed by monocyte-derived dendritic cells and presented to T cells. Oncotarget 2016, 7(45):73960-73970.
23. Schmitt M, Schmitt A, Rojewski MT, Chen J, Giannopoulos K, Fei F, Yu Y, Gotz M, Heyduk M, Ritter G et al: RHAMM-R3 peptide vaccination in patients with acute myeloid leukemia, myelodysplastic syndrome, and multiple myeloma elicits immunologic and clinical responses. BLOOD 2008, 111(3):1357-1365.
24. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette TR, Anstet MJ, Kincead-Beal C, Kulkarni P et al: Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. NEOPLASIA 2007, 9(2):166-180.
25. Mizuno H, Kitada K, Nakai K, Sarai A: PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC MED GENOMICS 2009, 2:18.
26. Tang Z, Kang B, Li C, Chen T, Zhang Z: GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. NUCLEIC ACIDS RES 2019, 47(W1):W556-W560.
27. Lanczky A, Nagy A, Bottai G, Munkacsy G, Szabo A, Santarpia L, Gyorffy B: miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat 2016, 160(3):439-446.
28. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. SCI SIGNAL 2013, 6(269):l1.
29. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E et al: The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. CANCER DISCOV 2012, 2(5):401-404.
30. Wang X, Zhang C, Yan X, Lan B, Wang J, Wei C, Cao X, Wang R, Yao J, Zhou T et al: A Novel Bioavailable BH3 Mimetic Efficiently Inhibits Colon Cancer via Cascade Effects of Mitochondria. CLIN CANCER RES 2016, 22(6):1445-1458.
31. Pullar CE, Chen J, Isseroff RR: PP2A activation by beta2-adrenergic receptor agonists: novel regulatory mechanism of keratinocyte migration. J BIOL CHEM 2003, 278(25):22555-22562.
32. Bardou P, Mariette J, Escudie F, Djemiel C, Klopp C: jvenn: an interactive Venn diagram viewer. BMC BIOINFORMATICS 2014, 15:293.
33. Schwertfeger KL, Cowman MK, Telmer PG, Turley EA, McCarthy JB: Hyaluronan, Inflammation, and Breast Cancer Progression. FRONT IMMUNOL 2015, 6:236.
34. Weigel PH, Hascall VC, Tammi M: Hyaluronan synthases. J BIOL CHEM 1997, 272(22):13997-14000.
35. Edward M, Gillan C, Micha D, Tammi RH: Tumour regulation of fibroblast hyaluronan expression: a mechanism to facilitate tumour growth and invasion. CARCINOGENESIS 2005, 26(7):1215-1223.
36. Dai Q, Liu T, Gao Y, Zhou H, Li X, Zhang W: Six genes involved in prognosis of hepatocellular carcinoma identified by Cox hazard regression. BMC BIOINFORMATICS 2021, 22(1):167.
37. Li Y, Qi D, Zhu B, Ye X: Analysis of m6A RNA Methylation-Related Genes in Liver Hepatocellular Carcinoma and Their Correlation with Survival. INT J MOL SCI 2021, 22(3).
38. Liu Y, Yin S: A Novel Prognostic Index Based on the Analysis of Glycolysis-Related Genes in Head and Neck Squamous Cell Carcinomas. J ONCOL 2020, 2020:7353874.
39. Sun Z, Liu C, Cheng SY: Identification of four novel prognosis biomarkers and potential therapeutic drugs for human colorectal cancer by bioinformatics analysis. J Biomed Res 2020, 35(1):21-35.
40. Kang H, Wang N, Wang X, Zhang Y, Lin S, Mao G, Liu D, Dang C, Zhou Z: A glycolysis-related gene signature predicts prognosis of patients with esophageal adenocarcinoma. Aging (Albany NY) 2020, 12(24):25828-25844.
41. Li Z, Qi F, Li F: Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma. INT J MOL SCI 2020, 21(22).
42. Xu F, Guan Y, Xue L, Huang S, Gao K, Yang Z, Chong T: The effect of a novel glycolysis-related gene signature on progression, prognosis and immune microenvironment of renal cell carcinoma. BMC CANCER 2020, 20(1):1207.
43. He Z, Mei L, Connell M, Maxwell CA: Hyaluronan Mediated Motility Receptor (HMMR) Encodes an Evolutionarily Conserved Homeostasis, Mitosis, and Meiosis Regulator Rather than a Hyaluronan Receptor. CELLS-BASEL 2020, 9(4).
44. Caon I, Bartolini B, Parnigoni A, Carava E, Moretto P, Viola M, Karousou E, Vigetti D, Passi A: Revisiting the hallmarks of cancer: The role of hyaluronan. SEMIN CANCER BIOL 2020, 62:9-19.
45. Skandalis SS, Karalis T, Heldin P: Intracellular hyaluronan: Importance for cellular functions. SEMIN CANCER BIOL 2020, 62:20-30.
46. Maxwell CA, Keats JJ, Belch AR, Pilarski LM, Reiman T: Receptor for hyaluronan-mediated motility correlates with centrosome abnormalities in multiple myeloma and maintains mitotic integrity. CANCER RES 2005, 65(3):850-860.
47. Assmann V, Jenkinson D, Marshall JF, Hart IR: The intracellular hyaluronan receptor RHAMM/IHABP interacts with microtubules and actin filaments. J CELL SCI 1999, 112 ( Pt 22):3943-3954.
48. Maxwell CA, Keats JJ, Crainie M, Sun X, Yen T, Shibuya E, Hendzel M, Chan G, Pilarski LM: RHAMM is a centrosomal protein that interacts with dynein and maintains spindle pole stability. MOL BIOL CELL 2003, 14(6):2262-2276.
49. Wittmann T, Boleti H, Antony C, Karsenti E, Vernos I: Localization of the kinesin-like protein Xklp2 to spindle poles requires a leucine zipper, a microtubule-associated protein, and dynein. J CELL BIOL 1998, 143(3):673-685.
50. Chen H, Mohan P, Jiang J, Nemirovsky O, He D, Fleisch MC, Niederacher D, Pilarski LM, Lim CJ, Maxwell CA: Spatial regulation of Aurora A activity during mitotic spindle assembly requires RHAMM to correctly localize TPX2. CELL CYCLE 2014, 13(14):2248-2261.
51. Chen H, Connell M, Mei L, Reid G, Maxwell CA: The nonmotor adaptor HMMR dampens Eg5-mediated forces to preserve the kinetics and integrity of chromosome segregation. MOL BIOL CELL 2018, 29(7):786-796.
52. Groen AC, Cameron LA, Coughlin M, Miyamoto DT, Mitchison TJ, Ohi R: XRHAMM functions in ran-dependent microtubule nucleation and pole formation during anastral spindle assembly. CURR BIOL 2004, 14(20):1801-1811.
53. Scrofani J, Sardon T, Meunier S, Vernos I: Microtubule nucleation in mitosis by a RanGTP-dependent protein complex. CURR BIOL 2015, 25(2):131-140.
54. Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Wang L, Silverstein MC, Ma'Ayan A: Massive mining of publicly available RNA-seq data from human and mouse. NAT COMMUN 2018, 9(1):1366.
55. Wang Z, Wu Y, Wang H, Zhang Y, Mei L, Fang X, Zhang X, Zhang F, Chen H, Liu Y et al: Interplay of mevalonate and Hippo pathways regulates RHAMM transcription via YAP to modulate breast cancer cell motility. Proc Natl Acad Sci U S A 2014, 111(1):E89-E98.
56. Sohr S, Engeland K: RHAMM is differentially expressed in the cell cycle and downregulated by the tumor suppressor p53. CELL CYCLE 2008, 7(21):3448-3460.

Tables

Table 1 Relation between HMMR expression and patient prognosis of different cancer (Overall Survival) in Prognoscan database.

Table 2 Kaplan-Meier plotter to determine the effect of different clinicopathological factors on the expression of HMMR gene and clinical prognosis in PAAD.