In Situ Analysis of N-Linked Glycans as Potential Biomarkers of Clinical Course in Human Prostate Cancer

Prostate cancer is the most common cancer in men and is the second leading cause of cancer-related mortality in men worldwide (1). Many factors contribute to the development and progression of prostate cancer including age, family history, ethnicity, and diet or lifestyle (2). Patient prognosis largely depends on tumor grade, more specifically referred to as the “grade group,” which is determined by microscopic histopathologic examination (3, 4). Although patients diagnosed with low-grade prostate tumors have a 99% 5-year survival rate, patients with higher grade tumors and those who present with distant metastasis have significantly poorer survival (3). Standard treatment for prostate cancer includes active surveillance for patients with low-grade tumors, localized therapy (radical prostatectomy and/or radiation) for intermediate and selected high-grade tumors, and hormone therapy for patients with recurrence or metastatic disease (2). Despite the prognostic correlation with tumor grade group, health disparities further contribute to prostate cancer patient outcomes. For example, Black males and men from rural Appalachia have a poorer prognosis even when diagnosed with low-grade prostate tumors (5–8). Further, preliminary genomic profiling studies have revealed distinct differences between patients with prostate cancer of African descent compared with European descent (9). One critical knowledge gap in prostate cancer biology is the molecular events underlying higher incidence and mortality rates within the Black and Appalachian patient populations, which could lead to better understanding of prostate cancer biology, predictive biomarkers, and personalized therapy.

N-linked glycosylation is a cotranslational event necessary for cell surface, secreted, and circulating proteins, wherein glycoconjugates containing N-acetylglucosamine (GlcNAc) are covalently attached to asparagine residues on the nascent carrier protein, followed by sequential addition of monosaccharides such as mannose, fucose, sialic acid, or GlcNAc (10). Several biological processes are regulated by N-linked glycosylation including cell adhesion, immune modulation, cell–matrix interactions, and cell proliferation (11–15). Recent glycomic and proteomic studies have revealed extensive alterations in both the N-glycan profile and glycosyltransferase expression of several human cancers, including breast, lung, and prostate (16). Moreover, aberrant N-glycosylation has been shown to directly facilitate epithelial-to-mesenchymal transition (EMT) and subsequent metastatic protentional of cancer cells by directly altering the activity of extracellular matrix proteins and growth factor signaling (17). Given the role of N-glycosylation during EMT and metastasis, defining the N-glycome of prostate tumors could provide insight into the molecular mechanisms driving prostate cancer progression and could be used to discover new biomarkers or potential novel therapies.

Matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) is a new and innovative technique in glycobiology that can be used to profile N-glycans with spatial distribution in formalin-fixed paraffin-embedded (FFPE) samples and high-throughput analysis of tissue microarrays (TMA; refs. 18, 19). This novel approach uniquely utilizes (i) the enzyme peptide-N-glycosidase F (PNGase F) that directly releases in situ N-linked glycans from glycoproteins, and (ii) α-cyano-4-hydroxycinnamic acid (CHCA) ionization matrix for detection of N-linked glycans by MALDI-MSI (20). Previous studies have revealed distinct alterations in the N-glycan distribution between normal and prostate tumor tissue (19, 21), with several of the N-glycan species elevated in prostate cancer being linked to EMT and metastasis (22, 23). Current MALDI-MSI analyses of prostate cancer tissues have utilized large prostate tissue sections and elegantly describe the N-glycan spatial differences between tumor and nontumor regions (19, 21). Given these recent findings and the role of N-glycans in EMT, we hypothesized that N-glycan profiling may have the potential to both define tumor grade and predict overall patient outcome in prostate cancer. We performed MALDI-MSI analysis on FFPE prostate cancer TMAs constructed from archived human prostate tissues from over 100 patients treated at the Markey Cancer Center. This patient data set included both cancer and matched normal tissue from racially and geographically diverse patients with over 10 years of follow-up metadata, allowing us to evaluate N-glycans as prognostic indicators for the long-term clinical course of prostate cancer progression.

We observed significant N-glycan dysregulation between benign prostate tissue and tumor prostate tissue with several glycans tracking either positively or negatively with tumor grade group. Specifically, high mannose as well as tri- and tetra-antennary branched N-glycans were more abundant within tumor tissue and correlated with increasing tumor grade. Further, we expanded our analyses to access glycosylation patterns in patient populations disproportionally affected by prostate cancer. We found statistically significant differences in the N-glycan profiles of low-grade group prostate cancer tumors between our cohort of Black and White patients. Moreover, we have identified a glycan signature that separates Appalachian patients who developed disease recurrence compared with those who remained disease-free. This striking data highlights fundamental differences in carbohydrate metabolism may represent a novel research strategy for the treatment of prostate cancer. Overall, our data suggest that aberrant N-linked glycosylation correlates with the clinical course of prostate cancer, which highlights the clinical potential of MALDI-MSI analysis for novel biomarker discovery, and emphasizes the need for personalized medicine for patients with prostate cancer.

High-performance liquid chromatography (HPLC)-grade acetonitrile, ethanol, methanol, water, CHCA, and trifluoroacetic acid (TFA) were purchased from Sigma-Aldrich. Histological-grade xylenes was purchased from Spectrum Chemical. Citraconic anhydride for antigen retrieval was obtained from Thermo Fisher Scientific. Recombinant PNGaseF Prime was obtained from Bulldog Bio, Inc.

TMAs were created from residual FFPE radical prostatectomy samples by the Biospecimen Procurement and Translation Pathology Shared Resource Facility (BPTP SRF) of the Markey Cancer Center (MCC) with approval from the institutional review board (IRB). These specimens were coupled with de-identified demographic and clinical data provided by the Cancer Research Informatics (CRI) SRF and the MCC with approval from the IRB. The TMAs contained prostate tumor tissue (n = 108 samples) and benign prostate tissue (n = 30 samples) from 138 patients. Clinical and demographic data are summarized in Table 1. All tissues were de-identified to the investigators.

FFPE TMA slides were sectioned at 4 μm on positively charged glass slides and processed as described previously (20, 24). In brief, tissues were dewaxed and rehydrated followed by antigen retrieval in citraconic anhydride buffer (25μl citraconic anhydride, 2 μL 12 M HCl, and 50 mL HPLC-grade water, pH 3.0–3.5). Recombinant PNGase F (0.1 μg/μL) was applied using an M5 TMSprayer Robot (HTX Technologies LLC). Enzyme was sprayed onto the slide at a rate of 25 μL/min with a 0 mm offset and a velocity of 1,200 mm/min at 45°C and 10 psi for 15 passes, followed by a 2-hour incubation at 37°C in a prewarmed humidity chamber. After incubation, slides were desiccated and 7 mg/mL CHCA matrix in 50% acetonitrile with 0.1% TFA was applied at 100 μL/min with a 2.5 mm offset and a velocity of 1,300 mm/min at 79°C and 10 psi for 10 passes using the M5 Sprayer. Slides were stored in a desiccator or immediately used for MALDI-MSI analysis.

A Waters Synapt G2-Si mass spectrometer (Waters Corporation) equipped with an Nd:YAG UV laser with a spot size of 50 μm was used to detect released N-glycans at X and Y coordinates of 75 μm. Spectra acquired were uploaded to high definition imaging (HDI) software (Waters Corporation) for mass range analysis from 750 to 3,500 m/z. For N-glycan quantification, regions of interest (ROI) were defined for the entire patient sample core on the TMAs using HDI image ROI drawing tool. For all pixels defined within each ROI, peak intensities were averaged and normalized by total ion current. For inter-sample normalization, the relative abundance for all N-glycans within each patient sample were normalized to the average total ion intensity as described in ref. 25. Representative glycan structures were generated in GlycoWorkbench.

Statistical analyses were carried out using GraphPad Prism 9.0 and Metaboanalyst 5.0. For Metaboanalyst multivariate analyses of all glycans, log transformation and auto scaling were used for normalization. Heatmaps were generated on the basis of the Euclidean distance measure and the Ward clustering algorithm. Glycans with variable importance in projection (VIP) scores >1.5 based on partial least squares discriminant analysis (PLS-DA) were selected for further analysis. For biomarker analysis, area under the curve (AUC) values were obtained using multivariate receiver operating characteristic (ROC) analysis based on the linear SVM classification method and SVM feature ranking method. For univariate analysis of individual glycans, all numerical data were analyzed using GraphPad Prism 9.0 and are presented as individual data points ± SEM. Column analyses were performed using Student t test with Welch correction when appropriate. A P value less than 0.05 was considered statistically significant.

TMAs containing human prostate tissue were created by the BPTP SRF of the Markey Cancer Center with approval from the IRB. Samples were coupled with de-identified demographic and clinical data provided by the CRI SRF of the Markey Cancer Center with approval from the IRB. Use of the tissue and de-identified information for the purpose of this study was given an exempt status from the IRB.

Previous MALDI-MSI analyses of prostate cancer tissue sections have revealed distinct differences in the spatial distribution of several species of N-glycans between tumor and nontumor regions (19, 21). We aimed to expand on these observations and utilized MALDI-MSI analysis to define the N-glycome of over 100 patients with prostate cancer with demographic information and clinical course. We obtained FFPE prostate TMAs containing both benign prostate tissue and prostate tumor tissue constructed from patients who underwent radical prostatectomy. This unique patient cohort with over 10 years of patient follow-up metadata allowed survival analysis against various clinical parameters (Supplementary Fig. S1). The TMAs analyzed included patient samples of prostate cancer grade groups 1 to 5, and clinicopathologic parameters included race, geographic location, as well as disease recurrence and patient survival (Table 1). Utilizing the modern grading system, few patients are diagnosed with grade group 4 prostate cancer at radical prostatectomy (26), and this fact is reflected in our cohort of patients with only three grade group 4 patient samples. Therefore, these samples were omitted from the analysis due to a low statistical power. TMA slides were prepared using a previously established MALDI-MSI workflow (19, 20). First, bound N-glycans were cleaved from glycoproteins by the addition of PNGaseF; then, the CHCA ionization matrix was applied uniformly using an HTX high velocity dry-spraying robot (27). Released N-glycans were analyzed using a Waters Synapt G2 ion-mobility enabled mass spectrometer equipped with an Nd:YAG UV laser (Fig. 1A). Ion mobility improved glycan detection by separating N-glycans from ionization matrix based on differential collision cross section (Fig. 1B). Using this method, we detected 46 N-glycans across all tissue samples (Fig. 1C; Table 2). Imaging capability allows the direct visualization of all patient tumor cores on the same scale. Representative HDI software images of four distinct classes of N-glycans: core fucose (1,809 m/z), high-mannose (1,743 m/z), bisecting (1,704 m/z), and sialylated (1,976 m/z) are shown in Fig. 1D to G, and exhibit a wide range of abundance across the TMA.

To investigate the unique N-glycan profile of prostate tumors, we first compared the N-glycome of benign and prostate tumor tissue. Multivariate analysis demonstrated the glycomic profile of prostate tumor tissue was moderately separated from benign by PLS-DA (Fig. 2A). The top 15 most discriminant N-glycans revealed by VIP analysis after PLS-DA are listed in Fig. 2B. Consistent with previous studies, we observed increases in both high-mannose (Fig. 2C–E; refs. 19, 21) as well as branched (Fig. 2F–H) N-glycans in prostate tumor tissue compared with benign. VIP analysis identified several additional discriminant N-glycan structures between benign and prostate tumor tissue including biantenarry complex, core fucosylated, bisecting, and sialylated, which were further analyzed by Student t test. We observed a significant decrease in both 1,663 m/z and 1,976 m/z, a bianetarry complex and sialylated N-glycan, respectively (Supplementary Figs. S2A and S2B). In addition, 1,501 m/z (bianetarry complex), 1,704 m/z (bisecting), and 1,809 m/z (core fucosylated) trend towards decreased abundance in prostate tumor tissue, however this was not found to be statistically significant (Supplementary Figs. S2C–S2E). We next utilized multivariate ROC analysis to investigate the utility of N-glycan profiling in distinguishing between benign and prostate tumor tissue. ROC analysis of the top 5 most discriminant N-glycans identified by VIP analysis yielded an accuracy of 0.612 [95% confidence interval (CI), 0.167–0.784; Fig. 2I]; however, ROC analysis using a panel of only high-mannose and branched N-glycans improved this to 0.676 (95% CI, 0.546–0.832; Fig. 2J). These findings reaffirm that aberrant N-linked glycans are clinical features of prostate cancers (19, 21).

Building on our initial observation between the benign and tumor N-linked glycan profile, we hypothesize that certain glycans will track with prostate grade groups. To test this hypothesis, we expanded our analysis to tumor grade group to identify specific N-glycan differences. Unsupervised clustering heatmap analysis demonstrated distinct N-glycan patterns between benign and grade group-specific tumors (Supplementary Fig. S2F). Notably, benign and grade group 1 tumors cluster together whereas grade group 3 and 5 exhibit similar N-glycan profiles. We observed an increase in high-mannose (Fig. 3A–C) and branched (Fig. 3D–F) N-glycans that positively correlated with tumor grade group. Further, we observed a grade group dependent decrease in both non-fucosylated (1,663 and 1,501 m/z; Supplementary Figs. S2G and S2H) and core fucosylated (1,809 m/z; Supplementary Fig. S2I) structures of several biantennary N-glycans in prostate tumor tissue compared with benign.

High-mannose glycans are produced early in the N-glycan biosynthetic pathway, wherein mannose residues are sequentially added to the growing glycan chain in the Golgi, followed by further processing by mannosidases and glycotransferases into more structurally diverse complex and hybrid glycans (28, 29). Excess levels of high-mannose glycans are routinely detected in cancer tissues and have been implicated in the progression of several human cancers including liver, lung, and breast (30–32). Further, tri- and tetra-antennary branched glycan structures have been linked to many aspects of tumorigenesis including neoplastic transformation, cell proliferation, and abnormal cell morphology (33). Moreover, it has been demonstrated that the addition of tri- and tetra-antennary branched glycans to E-cadherin impairs cell adhesion and promotes tumor cell invasion (34). Our findings demonstrate accumulation of these N-glycan structures correlate with tumor progression. In addition to being less abundant in prostate tumor tissue compared with benign, we found the abundance of the sialylated N-glycan 1,976 m/z decreases with tumor grade group (Supplementary Fig. S2J). Consistent with this observation, we found the abundance of the bisecting N-glycan, 1,704 m/z, decreases proportionally to tumor grade group (Supplementary Fig. S2K). Together, these changes represent a diverse class of N-linked glycans that have been previously attributed to participate in resistance to cell death (35), evasion of immune surveillance (36), and alterations of surface receptor ligand binding, dimerization, and signaling capacities (37–39). Collectively, our data suggest re-organization of the N-linked glycan profile within the prostate cancer tissues that correlates to the aggressive nature of higher grade group prostate cancer.

Higher tumor grade groups are typically associated with poorer patient outcomes and an increased likelihood of developing disease recurrence (3). Therefore, we hypothesized that specific alterations in N-linked glycosylation may predict the clinical course of prostate cancer progression. To assess this hypothesis, we took advantage of the 10-year follow-up metadata linked to the our patient cohort for survival analysis. First, we compared the N-glycan profile of prostate tumors between patients who did or did not have disease recurrence. Multivariate analysis demonstrated the glycomic profile of prostate tumor tissue from patients with disease recurrence significantly overlaps with patients who did not have disease progression by PLS-DA (Fig. 4A). The top 15 most discriminant N-glycans revealed by VIP analysis after PLS-DA are listed in Fig. 4B. Multivariate ROC analysis of the top 5 VIP scored glycans yielded an accuracy of 0.590 (95% CI, 0.456–0.697; ref. Fig. 4C). These glycan signatures could not robustly predict patients with recurrence with high accuracy; however, we identified one branched N-glycan (2,320 m/z) that was significantly more abundant in patients who had recurrence (Fig. 4D). Further, we stratified patients by low (below median) and high (above median) 2,320 m/z abundance and found high 2,320 m/z patients had poorer survival by Kaplan–Meier analysis (Fig. 4E).

It is well known that health disparities exist within prostate cancer patient cohorts, specifically in Black men and men from rural Appalachia (5–8). Thus, we expanded our analyses to these distinct patient populations to identify potential N-glycan signatures that could be utilized for biomarker applications. While increasing evidence suggests that molecular and genetic alterations contribute to the racial disparity between Black and White patients with prostate cancer (40, 41), robust and specific molecular features that correlate with accelerated disease progression in Black men remains largely unknown. Our TMAs included a modest cohort of Black patients with grade group 1 and 2 prostate tumors treated at the Markey Cancer Center, thus we expanded our analysis to examine the N-glycan profile of low-grade group tumors from Black and White patients with prostate cancer. Unsupervised clustering heatmap analysis demonstrated grade group 2 tumors between Black and White patients cluster together, whereas grade group 1 tumors are distinct between the two patient cohorts (Fig. 5A). This was further confirmed by PLS-DA analysis (Fig. 5B and C). Specifically, we observed increased abundance of several N-glycans in grade group 1 tumors from Black patients including branched (1,891 m/z), bisecting (1,866 and 2,174 m/z), and biantenarry complex (1,663 m/z; Fig. 5D–G). Due to low sample size of Black patients from our cohort with grade group 1 tumors (n = 4), we aimed to validate these findings in larger tumor tissues with additional spatial information. We identified one Black and one White patient diagnosed with grade group 1 tumors, treated by radical prostatectomy at the Markey Cancer Center, and analyzed the relative abundance of the four N-glycans found to be significant in our TMA cohort (Supplementary Figs. S3A and S3B). We confirmed an increase in all but one of the N-glycans (1891 m/z) in the Black patient tumor compared with White (Supplementary Figs. S3C–S3G). However, the abundance of all four N-glycans that were higher in the Black patient tumor were further elevated in adjacent benign tissue (Supplementary Fig. S4). Together, these data suggest the difference between grade group 1 prostate tumors from Black and White patients could be due to adjacent and intervening benign tissue.

Given the difference in N-glycan profiles between Black and White patients with low-grade group tumors, we hypothesized that changes in N-linked glycosylation may be a potential biomarker for disease progression in Black patients. Multivariate analysis demonstrated the glycomic profile of prostate tumor tissue from Black patients with disease recurrence moderately separates from Black patients who did not have disease progression by PLS-DA (Supplementary Fig. S5A). The top 15 most discriminant N-glycans revealed by VIP analysis after PLS-DA are listed in Supplementary Fig. S5B. Multivariate ROC analysis of the top 5 VIP scored glycans yielded an accuracy of 0.805 (95% CI, 0.314–1; Supplementary Fig. S5C). Further, we identified one sialylated N-glycan (2,341 m/z), which was significantly less abundant in patients who had recurrence (Supplementary Fig. S5D), although this did not correspond to a difference in disease progression by Kaplan–Meier analysis (Supplementary Fig. S5E). Together, we have identified potential differences in N-glycosylation between low-grade group tumors from Black and White patients, and unique N-glycans that associate with disease recurrence in Black patients that warrant further study.

Several epidemiologic studies have revealed patients with prostate cancer from rural Appalachia have poorer overall survival despite having lower incidence compared with patients from non-Appalachian counties (6, 8). Yet, the molecular mechanisms underlying this health disparity are largely unknown. Unsupervised clustering (Supplementary Fig. S6A) and PLS-DA analysis (Supplementary Figs. S6B–S6E) revealed no distinct differences between grade group-matched Appalachian patients compared with non-Appalachian patients. However, given the health disparity in patients with prostate cancer from rural Appalachia, we hypothesized specific changes in N-glycans within this population may contribute to poorer clinical outcomes. We compared the N-glycan profiles of prostate tumors between Appalachian patients who did or did not have disease recurrence. Multivariate analysis demonstrated the glycomic profile of prostate tumor tissue from Appalachian patients with disease recurrence is distinct from patients who did not have disease progression by PLS-DA (Fig. 6A). The top 15 most discriminant N-glycans revealed by VIP analysis after PLS-DA are listed in Fig. 6B. Multivariate ROC analysis of the top 5 VIP scored glycans yielded an accuracy of 0.849 (95% CI, 0.709–0.985; Fig. 6C), indicating we have identified an N-glycan panel with the potential to predict disease recurrence in Appalachian patients. Moreover, we identified a bisecting N-glycan (1,850 m/z) that was significantly more abundant in Appalachian patients who developed recurrence and correlates to significantly poorer overall survival by Kaplan–Meier analysis (Fig. 6D and E). Our data suggest N-glycan profiling could be an effective tool to predict prostate cancer recurrence and survival in disparate populations, and highlights the clinical potential of MALDI-MSI to study the health disparity in this distinct patient population.

An increasing body of evidence suggests that aberrant N-glycosylation plays a key role in several aspects of tumorigenesis, such as tumor cell invasion and metastasis, cell–matrix interactions, tumor angiogenesis, and cell signaling and communication (16). With the advent of new high-throughput mass spectrometry based technologies, such as MALDI-MSI, N-linked glycomic profiling of patient tumor tissues has demonstrated remarkable potential for early diagnosis, risk prediction, and treatment outcome for several cancers (42). Moreover, MALDI-MSI analysis of N-glycans provides insight into the function of N-linked glycosylation in tumor metabolism and cancer progression (43, 44). The use of TMAs is advantageous for high-throughput investigation during a single experiment using widely available FFPE patient samples, often including clinical follow-up data. In this study, we utilized prostate cancer TMAs including benign and tumor tissue resected from over 100 patients with 10 years of clinical follow-up metadata to perform N-glycan profiling by MALDI-MSI analysis. Given patients with low-grade prostate tumors have a 99% 5-year survival rate, having long-term patient follow-up data is an essential for survival analysis. Specifically, with longer clinical follow-up intervals, we were able to correlate N-glycans with disease recurrence and overall survival. We identified unique intratumoral glycan signatures that correlate with tumor grade group.

We observed significant dysregulations in multiple species of N-glycans between benign prostate tissue and prostate tumor tissue. Specifically, prostate tumors exhibit accumulation of high-mannose glycans, a common feature of human cancers that correlates with more aggressive cancer phenotypes, that increase with tumor grade group (16, 45). Accumulation of high-mannose N-glycans in prostate tumors suggests a lack of N-glycan trimming reactions and a decrease in mannosidase activity, or aberrant mannose metabolism (30–32). In addition, we found prostate tumors accumulate tri- and tetra-antennary complex glycans containing a core fucose moiety suggesting prostate tumors have enhanced GlcNAc metabolism. N-glycan β1,6-branching, which gives rise to these structures, has been implicated in several tumorigenic processes including neoplastic transformation, cell proliferation, and abnormal cell morphology (18, 33). Our findings suggest that increased N-glycan β1,6-branching and the accumulation of high-mannose glycans may contribute to prostate cancer progression. Further, we also identified several species of N-glycans that were elevated in benign tissue compared with prostate cancer tumors, including core fucosylated, bisecting, and sialylated glycans. Strikingly, biantennary complex glycans with a core fucose moiety are lower in prostate tumor tissue and decrease with tumor grade group, whereas tri-and tetra-antennary core fucosylated glycans are increased, suggesting that branching, rather than core fucosylation, may contribute to prostate cancer progression. Together, high-mannose and branched N-glycans show promise in distinguishing tumor versus benign prostate tissue. Future analyses should be extended to a wider spread of clinical behaviors (response to therapy, comorbidities, driver mutations, etc.) to define the full prognostic value of these specific glycans in prostate cancer.

Health disparities among different prostate cancer patient populations have been well documented, with men from rural Appalachia and Black men having higher mortality rates (5–7, 46, 47). Yet, the molecular mechanisms driving poorer patient outcomes in these distinct populations are largely unknown. Our patient cohort is unique in that it includes samples from both disproportionally affected populations. We observed statistically significant increases in several N-glycans in grade group 1 tumors from Black patients compared with White patients. However, larger tissue imaging analysis suggest the differences observed between the two groups extend in the adjacent and intervening benign tissue as well. Due to our limited access to additional low-grade samples from Black patients, this interesting phenotype warrants future investigation with an additional patient cohort. Moreover, we found a unique N-glycan panel that could identify Appalachian patients who had disease progression, of which, patients with higher abundance of 1,850 m/z had significantly poorer overall survival.

In summary, our data suggest that aberrant N-linked glycosylation is a molecular feature of prostate cancer, and highlights the application of MALDI-MSI N-glycan profiling as a promising prognostic tool. Moreover, our study reveals interesting differences between prostate tumors from both Black and White and Appalachian patient populations. Overall, these results warrant further investigation to define glycan metabolism and the regulatory mechanisms that contribute to aberrant protein glycosylation in prostate cancer, with an emphasis on defining the unique features of prostate tumors from Black and Appalachian patients, with respect to patient prognosis. Future studies should be expanded to include glycoproteomic analysis to define the specific proteins that are differentially glycosylated. Such studies can provide insight into the molecular drivers of prostate cancer progression and health disparities, which can be used to discover new biomarkers and novel personalized therapies.

This study employs mass spectrometry imaging to identify tumor-specific and patient demographic alterations in N-glycosylation in prostate cancer. Although MALDI-MSI is a powerful tool for high-throughput N-glycan profiling of a large number of patient samples, we are still limited by the patient cohort selected for this study. For our targeted demographic analysis, sample size was small for several groups; thus, future studies should include more patients to confirm our findings. Moreover, for the majority of the study, we analyzed prostate tumors from small tissue cores rather than larger tissue sections containing both tumor and nontumor stroma regions. As many tumor cores are not purely tumor tissue, this could contribute to increased variance in our results. Future analyses should be expanded to define N-glycosylation in different tumor regions defined by microenvironmental pressure in larger prostate tumor tissue sections.

L.R. Conroy reports grants from NIH during the conduct of the study. R.C. Sun reports employment with Maze Therapeutics. No disclosures were reported by the other authors.

L.R. Conroy: Data curation, formal analysis, investigation, visualization, writing–original draft, writing–review and editing. A.E. Stanback: Investigation, writing–review and editing. L.E.A. Young: Investigation, writing–review and editing. H.A. Clarke: Data curation, formal analysis, investigation, visualization. G.L. Austin: Data curation, formal analysis, visualization. J. Liu: Conceptualization, resources, data curation, formal analysis, funding acquisition, writing–review and editing. D.B. Allison: Conceptualization, resources, funding acquisition, writing–review and editing. R.C. Sun: Conceptualization, resources, funding acquisition, investigation.

This study was supported by NIH grant R01 AG066653 (to R.S. Sun), NINDS R21NS121966 (to R.S. Sun), St Baldrick's Career Development Award (to R.S. Sun), Rally Foundation Independent Investigator Grant (to R.S. Sun), V-Scholar Grant (to R.S. Sun), and NIH Training Grant T32CA165990 (to L.R. Conroy). This research was also supported by funding from the University of Kentucky Markey Cancer Center and the NIH-funded Biospecimen Procurement & Translational Pathology Shared Resource Facility, as well as the Cancer Research Informatics Shared Resource Facility, of the University of Kentucky Markey Cancer Center P30CA177558.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.