
==== Front
J Proteome Res
J Proteome Res
pr
jprobs
Journal of Proteome Research
1535-3893
1535-3907
American Chemical Society

38980715
10.1021/acs.jproteome.4c00099
Article
The Spatial Extracellular Proteomic Tumor Microenvironment Distinguishes Molecular Subtypes of Hepatocellular Carcinoma
https://orcid.org/0000-0002-8182-1366
Macdonald Jade K. †¶
Taylor Harrison B. †¶
Wang Mengjun †
Delacourt Andrew †
Edge Christin †
Lewin David N. †
Kubota Naoto ‡
Fujiwara Naoto ‡
Rasha Fahmida ‡
Marquez Cesia A. ‡
Ono Atsushi §
Oka Shiro §
Chayama Kazuaki ∥⊥#
Lewis Sara ∇
Taouli Bachir ∇
Schwartz Myron ∇○
Fiel M Isabel ∇◆
https://orcid.org/0000-0002-6285-6440
Drake Richard R. †
Hoshida Yujin ‡
https://orcid.org/0000-0002-9846-9389
Mehta Anand S. †
https://orcid.org/0000-0002-4436-555X
Angel Peggi M. *†
† Department of Cell and Molecular Pharmacology, Medical University of South Carolina, Charleston, South Carolina 29425, United States
‡ Liver Tumor Translational Research Program, Simmons Comprehensive Cancer Center, Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas 75390, United States
§ Department of Gastroenterology, Graduate School of Biomedical & Health Sciences, Hiroshima University, Hiroshima 734-8553, Japan
∥ Hiroshima Institute of Life Sciences, Hiroshima 734-8553, Japan
⊥ Collaborative Research Laboratory of Medical Innovation, Research Center for Hepatology and Gastroenterology, Hiroshima University, Hiroshima 734-8553, Japan
# RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
∇ Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
○ Department of Surgery, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
◆ Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
* E-mail: angelp@musc.edu.
09 07 2024
06 09 2024
23 9 37913805
12 02 2024
15 06 2024
31 05 2024
© 2024 The Authors. Published by American Chemical Society
2024
The Authors
https://creativecommons.org/licenses/by-nc-nd/4.0/ Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Hepatocellular carcinoma (HCC) mortality rates continue to increase faster than those of other cancer types due to high heterogeneity, which limits diagnosis and treatment. Pathological and molecular subtyping have identified that HCC tumors with poor outcomes are characterized by intratumoral collagenous accumulation. However, the translational and post-translational regulation of tumor collagen, which is critical to the outcome, remains largely unknown. Here, we investigate the spatial extracellular proteome to understand the differences associated with HCC tumors defined by Hoshida transcriptomic subtypes of poor outcome (Subtype 1; S1; n = 12) and better outcome (Subtype 3; S3; n = 24) that show differential stroma-regulated pathways. Collagen-targeted mass spectrometry imaging (MSI) with the same-tissue reference libraries, built from untargeted and targeted LC-MS/MS was used to spatially define the extracellular microenvironment from clinically-characterized, formalin-fixed, paraffin-embedded tissue sections. Collagen α-1(I) chain domains for discoidin-domain receptor and integrin binding showed distinctive spatial distribution within the tumor microenvironment. Hydroxylated proline (HYP)-containing peptides from the triple helical regions of fibrillar collagens distinguished S1 from S3 tumors. Exploratory machine learning on multiple peptides extracted from the tumor regions could distinguish S1 and S3 tumors (with an area under the receiver operating curve of ≥0.98; 95% confidence intervals between 0.976 and 1.00; and accuracies above 94%). An overall finding was that the extracellular microenvironment has a high potential to predict clinically relevant outcomes in HCC.

hepatocellular carcinoma
extracellular matrix
mass spectrometry imaging
collagen
proteomics
post-translational modification
proline hydroxylation
cancer
microenvironment
National Cancer Institute 10.13039/100000054 P30CA138313 Cancer Prevention and Research Institute of Texas 10.13039/100004917 RR180016 National Institute of Diabetes and Digestive and Kidney Diseases 10.13039/100000062 P30DK123704 National Institute of General Medical Sciences 10.13039/100000057 P20GM103542 National Cancer Institute 10.13039/100000054 U01CA288375 National Cancer Institute 10.13039/100000054 U01CA283935 National Cancer Institute 10.13039/100000054 R21CA263464 National Cancer Institute 10.13039/100000054 R01CA282178 National Cancer Institute 10.13039/100000054 R01CA255621 National Cancer Institute 10.13039/100000054 R01CA253460 National Cancer Institute 10.13039/100000054 R01CA233794 document-id-old-9pr4c00099
document-id-new-14pr4c00099
ccc-price
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pmcIntroduction

Hepatocellular carcinoma (HCC) affects millions of people globally, with thousands of new diagnoses per year, and accounts for the third highest cancer mortality rate.1−3 HCC incidence has tripled and mortality has more than doubled between 1975 and 2010 in the United States alone.4,5 The long-term survival of US patients with HCC is low, with an estimated 36% 5-year survival in localized cases, dropping to 3% 5-year survival when HCC has metastasized.4,6 Chronic liver diseases, including hepatitis C virus (HCV) and hepatitis B virus (HBV), alcoholic fatty liver disease, and nonalcoholic fatty liver disease (NAFLD), greatly increase the risk of developing HCC.7,8 HCC exhibits a high degree of pathological and molecular variability from patient to patient, limiting diagnosis and treatment stratification.9−12 The World Health Organization estimates that only 35% of HCC tumors can be diagnosed based on architectural abnormalities appearing in magnetic resonance imaging (MRI), contrast-enhanced CT scans, and pathological features of tissue.13

Multiple groups have worked on molecular subtyping of HCC to improve early detection, predict outcomes, and direct appropriate treatment.11,12,14−20 The studies have classified tumors based on genetic expression,17 combined mutational, miRNA, mRNA clusters,18 transcriptional pathways,12,15,16 and immune cell content.21 The Hoshida model characterizes HCC into three subtypes applicable to patients of both Western and Eastern origin.12,18,22 The model correlates transcription expression from tissue with clinical parameters of tumor size, cell differentiation, and serum alpha fetoprotein levels, resulting in three molecular HCC subtypes.12 Subtype 1 (S1) shows activation of the TGFβ1 fibrogenic pathways and is more clinically disseminative; Subtype 2 (S2) primarily activates angiogenic pathways through increases in sonic hedgehog (SHH) and is clinically more aggressive; Subtype 3 (S3) is regulated by liver-specific Wnt with somatic mutations in CTNNB and generally exhibits differentiated pathology. Subtypes 1 and 2 show poor outcomes (40% 5-year survival) compared to better outcomes in Subtype 3 (>80% 5-year survival).23 While many studies cover transcriptional details for subtyping, less is known about the surrounding stroma in HCC subtypes.

Fibrotic, collagenous extracellular matrix deposition within the stroma is the primary hallmark in the progression from a healthy liver through fibrosis, cirrhosis, and the development of hepatocellular carcinoma. Hepatic stellate cells (HSC) are considered the source of collagen stromal signatures. Furthermore, HSC stromal gene expression patterns are linked to patient prognosis.24 Once HCC has developed, multiple studies have shown that transcriptomic, proteomic, and histopathologic signatures from collagen stroma, found in either tissue or serum, have predictive value in long-term survival.23−28 Collagen expression in HCC is well-defined at the transcription level. Increases in collagen type I are involved in tumor growth and metastasis,29 while type III was shown to be elevated typically in early HCC progression.30 Post-translational modifications (PTM) on collagen, specifically proline hydroxylation (HYP), have been associated with HCC prevalence through the involvement of prolyl hydroxylase (PH) enzymes.31,32 The importance of proline hydroxylation in collagens is illustrated by the large number of variably hydroxylated sites in collagen types I, II, and III, which contribute to differential protein folding and stability.33−36 Alteration of HYP sites may be linked to the pathology of HCC, but knowledge of the sources of this regulation is currently limited.

In this study, we apply novel spatial proteomic approaches to target and investigate the proteomic extracellular microenvironment of molecularly subtyped Hoshida S1 and S3 HCC tumors that display differentiation of stroma pathways in subtypes. We hypothesize that at the translational level, collagen and extracellular matrix signatures spatially link to HCC tumors and differentiate between HCC subtypes. We further hypothesize that these differentiating sequences contain specific variations of post-translational proline hydroxylation. An initial finding was that the proteomic extracellular matrix signatures localized to pathologically annotated features of fibrosis, cirrhosis, and tumors, signifying a pathologically relevant proteomic readout. Further investigation into individual peptide analysis showed significant differences in extracellular matrix peptides and collagen domains that were post-translationally modified with hydroxylated prolines across tumor subtypes. This work reports molecular data beyond the genetic and transcriptomic signatures, providing fundamental information about the translational and post-translational signatures of the functional microenvironment. Novel extracellular peptide biomarkers present additional strategies to stratify patients for treatments and improve or monitor targeted therapeutics.

Experimental Procedures

Materials

Ammonium bicarbonate, α-cyano-4-hydroxycinnamic acid, ammonium hydroxide, ammonium phosphate, calcium chloride, citraconic acid, trifluoroacetic acid, and [Glu1]-fibrinopeptide B were purchased from Sigma-Aldrich (St. Louis, MO). Acetonitrile, formic acid, 0.1% formic acid in acetonitrile, xylenes, ethanol, methanol, hydrochloric acid, Trizma, and HPLC-grade water were purchased from ThermoFisher Scientific (Pittsburgh, PA). Collagenase Type III was purchased from Worthington Biochemical Corp. (Lakewood, NJ).

Samples

Deidentified formalin-fixed paraffin-embedded (FFPE) tissues (5 μm thick) of waste surgical HCC specimens were obtained from Hiroshima University Hospital and Mount Sinai Hospital through the University of Texas Southwestern (Institutional Review Board approval: STU062018–058). The study was approved as exemption no. 4 by the Institutional Review Board at the Medical University of South Carolina. The tissues were previously subtyped based on the Hoshida classification system.12,22 A total of 36 slides from different patients, categorized into either the S1 (n = 12) or S3 (n = 24) subtype, were analyzed for this study. The tissues contained various pathologies, including normal adjacent tissue, fibrosis, cirrhosis, and tumors, which were annotated by a pathologist.

MALDI MSI Tissue Preparation

The tissues had previously been dewaxed, deglycosylated, and reported for N-glycan subtyping.37 Tissues were stained with hematoxylin and eosin for pathology recording prior to ECM imaging.38,39 Coverslips were removed from tissue slides by soaking in xylene for 1–7 days. Additional washes were performed through sequential soaking in fresh xylenes (two times for three min each), 100% ethanol (1 min), Carnoy’s solution (60% ethanol, 30% chloroform, 10% acetic acid; twice for three min each), and 100%, 95%, and 70% ethanol for 1 min each. After drying, tissues were subjected to pressurized heat (95 °C) for 20 min in tris-buffer (10 mM, 1 mM CaCl2, pH = 9) to expose the collagen epitopes for efficient enzymatic digestion. Following epitope retrieval, the tris-buffer was sequentially exchanged with HPLC-grade H2O by diluting three times prior to drying via a desiccator. Collagenase type III was used for localized digestion of the extracellular matrix as done previously.39−44 In addition to the manufacturer’s characterization, collagenase type III was characterized by collagen activity assays (Abcam ab196999) prior to use. The absence of tryptic activity was validated by using a colorimetric trypsin activity assay kit (Abcam ab102531). An M3 TM-Sprayer was used to apply 0.1 mg/mL collagenase type III in 10 mM ammonium bicarbonate and 1 mM calcium chloride solution (pH = 7.35). The automated sprayer parameters were set as follows: 15 passes at 25 μL/min, nozzle at 40 °C and 40 mm distance from nozzle tip to slide surface, 10 psi nitrogen gas, crisscross pattern, 2.5 mm offset, and 1300 mm/min. The tissues were transferred to a humidity chamber at ≥90% relative humidity and incubated at 37.5 °C for 5 h to facilitate collagen digestion. After digestion, 7 mg/mL α-cyano-4-hydroxycinnamic acid (CHCA) matrix in 50% acetonitrile and 1% trifluoroacetic acid containing 0.15 pm/μL internal standard [Glu1]-fibrinopeptide B (Sigma-Aldrich) was applied using the M3 TM-Sprayer and an automated pump (70 μL/min, 10 passes, 79 °C, and 40 mm distance from nozzle tip to slide surface, 10 psi nitrogen gas, 3 mm offset, crisscross pattern). Immediately following the CHCA application, slides were dipped in a 4 °C 5 mM ammonium phosphate, monobasic solution and dried in a desiccator for a minimum of 5 min before imaging acquisition.

MALDI-MSI Acquisition and Data Extraction

MALDI-MS was performed using a MALDI QTOF (timsTOF-fleX, Bruker Daltonics) in positive ion mode with a m/z range of 700–2500, 75 μs transfer time and 20 μs prepulse storage, 100–150 μm step size, and 300 laser shots per pixel. Peak picking, histological mapping, and analysis were performed using SCiLS Lab 2022b (Bruker Daltonics, Bremen, Germany) after normalizing to total ion current (TIC). Pathologist-annotated H&E stains were coregistered with the acquired MSI image and used to computationally export the data pixels within the annotated region with a ± 20 ppm peak width using peak area interval processing mode and maximum peak intensity mean spectrum statistics.

LC-MS/MS Sample Preparation

Same tissue sections used for imaging studies were used for sequencing proteomics. Matrix was removed from slides (n = 4 subtype 1 and n = 4 subtype 3) with sequential soaking in decreasing concentrations of USP grade 200 proof ethanol (100%, 95%, 60% for 1 min each), HPLC water (1 min), 10 mM Tris base solution (pH 9.01, 1 min), HPLC water (1 min), citraconic buffer (pH 3, 1 min), and HPLC-grade water (3 min). Each tissue section was dried on-slide and carefully scraped into a low-binding centrifuge tube transported into an ammonium bicarbonate (10 mM) and calcium chloride (3 mM) solution in LC-MS-grade water (200 μL, pH = 7.4). The samples were sonicated in a benchtop sonicator (Branson Cleaning Equipment Company, Shelton, CT) for 2 h prior to ultrasonication. Ultrasonication was performed at 35% amplitude for 5 min with a 20-s pulse time on and 5-s pulse time off (Fisherbrand Model 505 Sonic Dismembrator attached to a Qsonica CL-18 sonicator probe). Additional sonication was performed at 70% amplitude for 5 min with a 30-s pulse time on and 3-s pulse time off. Collagenase type III (5 μg) was added to each solution, and samples were adjusted to 300 μL in a prepared buffer. Collagens and other extracellular matrix proteins were digested overnight at 38 °C with shaking at 450 rpm (Eppendorf Thermomixer). After digestion, samples were sonicated in a benchtop sonicator for 1 h and additional collagenase type III (5 μg) was added to each sample to complete digestion.45 Samples were then incubated for an additional 5 h at 38 °C with shaking at 450 rpm for further digestion. Samples were then pelleted (20,080 rcf, 15 min, Eppendorf Centrifuge 5417C) and the supernatant (200–250 μL) was removed and lyophilized in a Savant Speed Vac Concentrator for 6 h. Samples were then STAGE tipped (StageTips, Cat. No. SP301, Thermo Scientific) following the manufacturer’s protocol using LC-MS-grade solutions and eluate (150 μL) was dried under vacuum for 3 h (Speedvac). After drying, the samples were adjusted to 20 μL of 0.1% formic acid/HPLC-grade water and further purified following manufacturer’s protocols (ZipTip, SigmaAldrich, C18).

LC-MS/MS-Based Sequencing of Extracellular Peptides

An Easy nanoLC 1200 system (ThermoFisher Scientific) coupled to an Orbitrap Exploris 480 mass spectrometer (ThermoFisher Scientific) was used to collect sequence information. Prior to the start of each sample set, system suitability for proteomics was evaluated using 2 μg Pierce HELA protein digest standard (Thermo Scientific) in DDA mode to verify detection of >4750 proteins with 2 or more peptides at ≤1% false discovery rate. All sample injections are followed by blank injections to eliminate carryover. One microgram of the recovered peptides from each sample was injected onto a C18 column (Acclaim PepMap C18, i.d. 75 μm, 250 mm length, 2.0 μm, 100 Å). The peptides were eluted using a mobile phase gradient from 0% to 35% phase B (formic acid/acetonitrile 0.1/99.9, v/v) over 180 min (from 0 to 65 min). Mobile phase A consisted of formic acid/water (0.1/99.9, v/v), and the flow rate was set at 300 nL/min. Each gradient included a high organic wash up to 90% B at 190 min, held for 10 min, followed by returning to initial conditions at 205 min to re-equilibrate for 20 min. For each run, MS1 data were collected using an Orbitrap with a m/z range of 375 to 1700 (60,000 resolution; maximum injection time 50 ms; normalized AGC target at 300% with a minimum intensity filter at 8000). Orbitrap resolution for ddMSnScan was set to 15,000 over a scan range 200–1700 with 20 s dynamic exclusion; the normalized AGC target was set to 100%, and the charge state filter was set to include 1. MS2 scans were performed in the ion trap using higher-energy collision dissociation (HCD) fragmentation (isolation window 2 Da; NCE 33%; maximum injection time 120 ms; AGC target at 100%). Charge state filters were set to include 2–6, and dynamic exclusion duration was set to 20 s after one time within a 10 ppm window. Data were searched using MaxQuant as previously done, with a nonspecific enzyme search. Parameters included a peptide spectrum match, proteins, and site false discovery rate set to 0.01, minimum peptide length set to 7, minimum number of peptides set to 1, minimum peptide score of 70, and reporting of site modification probabilities for deamidation (NQ), hydroxylation (P), and oxidation (MP).39,45−48 A curated database with 1783 entries based on GeneOntology: 0031012 and keywords (collagen, elastin, aggrecan, gelatin, osteonectin, perlecan, and fibronectin) was downloaded from Uniprot on May 8, 2017. Reverse decoys were included to employ the target-decoy search strategy.49 Contaminants from MaxQuant were included to verify proper sample preparation. LC-MS/MS data provide a reference library for the imaging data and peptides were further filtered to score >70 for comparison with imaging data. Putative peptide identities were assigned to imaging data by accurate mass matching to resection image data, with accurate mass matching ≤8 ppm. In the case of multiple LC-MS/MS peptides with the same accurate masses and mapped to the same m/z value in MALDI-MSI results, both peptides were reported. Previous databases of extracellular and collagen peptide sequences found by the method in liver tissue39,45−48 were used to further support peptide identifications.

Targeted LC-MS/MS-Based Sequencing of Extracellular Peptides

Targeted runs were done on an Orbitrap Fusion Lumos as previously described41 against a targeted list. Targeted runs used a high-resolution (60,000) FTMS survey scan over a mass range of m/z 375–1575. This was followed by tandem mass spectrometry fragmentation of the precursors (charge states +1 to +6) with a cycle time of 3 s. An automatic gain control target value of 4.0e5 was used for the survey MS scan. Targeted inclusion was set to a mass tolerance window of 10 ppm, and a precursor isolation window of 0.8 m/z was used with the maximum injection time of 40 ms, and a 35% collision energy by HCD with fragments detected in the Orbitrap 15,000 resolution. Once fragmented, target precursors were placed on an exclusion list for a duration of 25 s. Apex detection and advanced peak determination were not enabled.

Statistical Analysis Image Data

Image peak intensities were exported using data visualization software (SCiLS 2022b, Bruker) using maximum peak intensities. A data score threshold of 50,072 was used to select peak widths of 20 ppm. Peaks were then filtered to remove isotopic peaks and matrix peaks based on the mass defect. Filtered peaks were used to perform heuristic image segmentation using bisecting k-means and Manhattan metric on spectra normalized to the total ion current. Statistical analysis was conducted using GraphPad Prism Version 9.4.0 where annotated. Metaboanalyst50 and clustervis51 were used for comparative visualization between data from specific pathological regions.

Machine Learning

R packages used were randomForest(v4.7) for random forest modeling, pROC(v1.18) for AUROC analysis, rstatix (v0.72) and WRS2(v1.1) for mixed-ANOVA and robust-mixed-ANOVA, respectively; and lme4(v1.1) and robustlmm(v3.2) for mixed regression analysis. Initial algorithm modeling performance was measured by applying a customary feature selection strategy using filter, wrapper, and embedded methods to combat artifactual data that might arise due to organizing and analyzing high dimensional data. Peptide selection for exploratory machine learning was done in an unbiased fashion on peptides based on m/z. In the filter step, the Mann–Whitney U test or t test was calculated for each variable and AUROC was assessed for each variable. We filtered out variables with a high p-value or a low AUROC. Cutoffs were used during different iterations of feature selections. The Gini index and variance of each variable provided information for further filtering. For highly correlated variables (r > 0.9), the variable with the highest AUROC was kept and this efficiently eliminated redundant variables. Initial performance was measured by a predictive value of AUROC ≥ 0.7 and a p-value ≤0.01 and sets that were performed above this were retained for random forest modeling. For training, the data set pixels derived from tumor were randomly split into a 3/4 training set and a remaining 1/4 data set into testing set. The training set was used to build a random forest with 500 trees and default mtry. The relative importance of each predictor was extracted and ranked to remove low informative predictors. The ntree and mtry were fine-tuned with a nested loop program and 3-fold cross-validation to evaluate models with best performance (sensitivity, specificity, accuracy >80%) and were regarded as the final models. Final exploratory random forest models were used to estimate accuracy, designating each subject in the validation set with S1 as positive, S3 as negative, and a predictive probability cutoff for AUROC at 0.5; peptide sets with AUROC ≥ are reported as examples.

Results

Overview of the Study and Sample Characteristics

A primary goal of the study was to investigate potential predictive values of spatially defined proteomic collagen and other extracellular matrix proteins differentiating between subtypes of poor outcome (S1) and subtypes of better outcome (S3). The Hoshida subtyping system was used because of its association of upregulated S1 gene pathways with extracellular matrix deposition, as well as characterized differential clinical outcomes between S1 and S3. Subtyped hepatocellular carcinoma tissues were evaluated for spatial differences in extracellular matrix signatures using previously published approaches targeting collagens and extracellular matrix.41,45−47,52 MALDI-MSI data provided spatial information, and LC-MS/MS data served as a reference library to provide peptide sequencing information. From 50 tissues, 36 were defined by subtype and evaluated between Subtype S1 (n = 12; 266,987 data total pixels) and Subtype S3 (n = 24; 796,066 total data pixels) using MSI. A subset of four tissues from each subtype was further evaluated using LC-MS/MS to provide a reference library for the imaging data. Samples were subtyped using the Hoshida approach and their N-linked glycosylation patterns were shown to differ across subtypes in a previous study.12,22,37,53 Annotated pathologies from tumor sections included regions of fibrosis, cirrhosis, tumor, necrosis, and normal adjacent tissue (Table S1). Necrosis appeared more common in tissues from the S1 subtype, while cirrhosis was more prevalent in the S3 tissues. Bridging fibrosis consistently comprised a smaller area of the tissue compared with the other analyzed pathologies as anticipated in tumor resections. The percent of tumor area per total tissue was not significantly different between subtypes (p = 0.561). This exploratory study characterizes the spatial ECM proteome and provides insight into understanding the molecular changes associated with outcomes of hepatocellular carcinoma.

HCC Tissues Show Spatial Distribution of Extracellular Peptide Signatures

Molecular and histological changes within the extracellular microenvironment are hallmarks of liver disease progression to HCC. However, the spatial regulation of the extracellular microenvironment within and across subtypes remains unknown. To determine proteomic spatial regulation in S1 and S3 tissues, pathological annotations (Figure 1A) were paired with targeted extracellular matrix mass spectrometry imaging (ECM-MSI) proteomics and LC-MS/MS sequencing workflows (Figure 1B). By ECM-MSI, a total of 3587 peaks were reported across tissue sections using a data score threshold of 50,072. After filtering out isotopic and matrix peaks by mass defect, 466 peaks remained. Image segmentation was used as an unbiased initial approach to visualize the MALDI-MSI proteomic readout. When compared to pathologically annotated hematoxylin and eosin (H&E) stains, proteomic clusters spatially localized to pathological features across all tissues (Figure 1C, Figure S1). By heuristic segmentation, each tumor section displayed defined regions corresponding to pathological annotations, particularly fibrotic regions, yet overall displayed high heterogeneity within tissue sections (Figure S1). Individual peptides that were identified by LC-MS/MS as domains from the fibrillar collagen structure localized to pathological features (Figure 1D–F). Both unmodified (Figure 1D,E) and modified (Figure 1F) peptides showed unique spatial localization based on intensity patterns. The patterns of individual peptides support the main premise of the study where collagen domains that have specific cell and protein binding capabilities are differentially regulated across the tissue.54,55 Overall, peptides from extracellular proteins showed spatial distribution that formed heterogeneous proteomic clusters and heuristically mapped to pathological annotations.

Figure 1 Spatial analysis of the proteomic extracellular microenvironment in hepatocellular carcinoma by subtype. (A) Hematoxylin and eosin stain summary from patient tissue section cohort with pathological annotations showing features of normal (black), fibrosis (blue), cirrhosis (purple), tumor (red), and necrosis (green). (B) Workflow summary used to analyze the tissues. Following spatial imaging to produce peptides from extracellular matrix proteins, a representative subset was analyzed by sequencing proteomics. (C) Examples of imaging segmentation results. Heuristic segmentation clustered pixels by individual spectra using the bisecting k-means method and Manhattan metric. Different colors represent different proteome regions. Segmentation clusters show the relationship between regions with the number of spectra that define each region. (D)–(F) Examples of single peptide sequences from fibrillar collagens showing spatial localization corresponding to tissue pathologies. Red on the intensity scale indicates regions of highest intensity, and blue indicates regions of lower intensity. AA – amino acid position.

Collagen α 1(I) Chain Discoidin Domain-Containing Receptor 2- and Integrin-αβ-Binding Domains are Spatially Localized but do not Differentiate S1 and S3 HCC Tumors

Hydroxylation of proline within specific collagen domains increases the level of protein binding, thereby altering cell signaling. Discoidin domain receptors (DDR1 and DDR2) binding to collagen56 activate signaling pathways that contribute to cancer processes of growth and metastasis.57−59 Likewise, integrin αβ binding to collagen60 has been well-defined to increase cancer stemness, progression, and contribute to metastasis.61,62 Probing for domains related to DDR binding (motif GVMGFP, where P is hydroxylated)63 or integrin αβ binding (motif GFPGER, where P is hydroxylated)64 reported image data coverage of both binding domains (DDR binding, m/z 1179.557, abbreviated P1179) and (integrin αβ binding, m/z 1588.792, abbreviated P1588) (Figure 2 and S2). Both sequences were identified as having a site of hydroxylated proline corresponding to the binding sequence, and unmodified versions were not detected in the sequencing data. Primary isoforms for the DDR- and integrin-binding domains were hydroxylated at proline 583 and proline 681, respectively (Figure S2). Image data of the DDR domain-binding peptide P1179 demonstrated heterogeneous spatial localization within both S1 and S3 subtypes, appearing broadly throughout the tissue (Figure 2B). The integrin-binding domain P1588 appeared as intense, highly localized expression within S1 tumors, while in S3 tissue, elevated expression patterns were observed surrounding the tumor (Figure 2C). Combined ion images highlighted complementary patterns across the tissues (Figure 2D). Across all samples, there was no significant difference in the expression levels of P1179 or P1588 over the entire tissue or in the tumor-extracted region (Figure 2E,F). However, data extracted from tumor regions showed a trend of DDR-binding domain P1179 increasing in S1 tumors (Figure 2E). In conclusion, the expression of collagen domains demarking sites for DDRs and integrin αβ binding was spatially localized to the defined regions but was not significantly altered between subtypes. To our knowledge, this is the first time that these collagen domains have been spatially mapped in clinically derived tissues related to hepatocellular carcinoma.

Figure 2 Collagen domains for discoidin domain receptor and integrin binding are spatially localized. (A) Hematoxylin and eosin staining of tumor sections. Tumors are outlined in red. (B) DDR-binding domain spatial distribution. The expression is diffuse through the whole tissue and appears within the tumor and within bridging fibrosis. (C) Integrin-αβ-binding domain expression is diffuse in S3 and localized largely to the region around the tumor in S3. (D) Combined ion images for DDR- and integrin-αβ-binding domains show complementary spatial patterns. DDR-binding domain intensity is green, and the integrin-binding domain intensity is pink. (E) DDR-binding expression across the whole tissue (left) or extracted from tumor regions (right). (F) Integrin-αβ-binding domain expression levels across the entire tissue or within the tumor. There was no significant difference in S1 versus S3 when comparing across the entire cohort. The Mann–Whitney Up-value is shown. Outlier tests reported no outliers.

Fibrinogen Peptides are Spatially Distributed and Differentiate Between Subtypes

Increased fibrinogen levels in serum and plasma have been associated with advanced tumor stage and poor survival in hepatocellular carcinoma.43,65,66 Through LC-MS/MS sequencing, data showed higher proportions of fibrinogen sampled from S1 compared to S3 (S1 753 spectra, 15.7%; S3 99 spectra; 2.6%) (Figures 3A and S3). Two fibrinogen peptides that matched the image data showed significant elevation in peak intensity in S1 tumors compared with S3 tumors. The fibrinogen alpha chain (FGA) peptide 1942.899 (P1942) was significantly increased 2.3-fold in S1 tumors compared to S3 (Figure 3B). The fibrinogen beta chain (FGB) peptide 1767.924 (P1767) showed a 3.2-fold increase in S1 tumors (Figure 3C). Significant areas under the receiver operating curves (AUROC > 0.8) suggested that these peptides have the potential to differentiate between the subtypes. Both peptides were identified as unmodified in the data set.

Figure 3 Fibrinogen peptides are spatially distributed throughout S1 tumors but not in S3 tumors. (A) Comparison of LC-MS/MS sampling of extracellular matrix composition. Counts of peptides mapped to fibrillar collagens (blue), fibrinogen (red), other collagens (dark gray), or other extracellular proteins (light gray) are presented in each pie chart from S1 (red, top) or S3 (blue, bottom) tissues. Additional pie charts depict the distribution of peptide counts for fibrillar collagens (top, blue) and fibrinogen proteins (bottom, red). (B) Box plots of peak intensity extracted from tumor regions and receiver operating characteristic (ROC) curves of fibrinogen beta peptide (m/z = 1767.924) and (C) fibrinogen alpha peptide (m/z = 1942.899). Mann–Whitney Up-values are shown. (D) Examples of hematoxylin and eosin staining with tumor annotations shown in red. (E) Spatial distribution of fibrinogen beta peptide (m/z = 1767.924) shown throughout S1 tumors while appearing around the borders of S3 tumors. (F) Spatial distribution of fibrinogen alpha peptide (m/z = 1942.899) within S1 tumors and around the borders and bridging fibrosis in S3 tumors. Red indicates high peptide intensity, while blue indicates low peptide intensity. (G) Combined ion image overlay of FGB peptide (P1767, pink) and FGA peptide (P1942, green).

Analysis of the spatial distribution of the target fibrinogen peptides supported higher expression in S1 tumors (Figure 3D–F). Both FGA and FGB fibrinogen peptides were differentially distributed within tumors. In S1 tissues, the FGB P1767 and FGA P1942 peptides showed increased but diffuse expression within the tumor, appearing with higher intensity around the tumor edges. S3 tumors showed low expression levels of peptides as reported by relative quantification. However, in S3 tissues, both P1767 and P1942 appeared with higher intensity in areas of bridging fibrosis and were detected with the lowest expression in the tumor regions. Complementary expression was observed, suggesting different roles for the fibrinogen domains (Figure 3G). In summary, pairing the spatial data with sequencing data demonstrated that fibrinogen peptides were localized to the S1 tumors and appeared in the bridging fibrosis of S3 tumors. Data extracted from tumor regions reported elevation within S1 tumors, corresponding with previous reports of higher fibrinogen serum levels66 associated with poor outcomes in hepatocellular carcinoma.

Fibrillar Collagen Hydroxylated Proline Modifications Define Subtypes

Previous work has earmarked the fibrillar collagens Col1a1, Col1a2, and Col3a1 as potential biomarkers related to progression and metastasis of hepatocellular carcinoma.29,30,67,68 To further explore collagen domain regulation, Col1a1, Col1a2, and Col3a1 sequenced peptides from S1 and S3 tumors were evaluated (Figure 4). Image data matched to LC-MS/MS reference libraries of peptides by high mass accuracy (<10 pm) resulted in 58 peptide matches (22 peptides Col1a1, 15 peptides Col1a2, 21 peptides Col3a1) (Figure 4A, Table S2 and Figure S44). Statistical evaluation demonstrated significant differences in 9 out of 12 unmodified collagen peptides and 38 out of 46 peptides with at least one HYP modification from fibrillar collagens mapped to the tumor image data (Figure 4B,C and Table S2). Principal component analysis (PCA) demonstrated that unmodified fibrillar collagen peptides did not separate based on subtype (Figure 4D). However, PCA did report the separation of subtypes based on the peak intensities of HYP-modified peptides within each group (Figure 4E). The cumulative expression of all peptides from Col1a1 and Col1a2 was higher in S1 tumors; unmodified peptides altered between S1 and S3, while HYP-modified peptides showed a larger elevation in S1-type tumors (Figure 4F and G). For both subtypes, unmodified Col3a1 showed a lower expression, while peptides containing hydroxylated proline showed a significant increase in the expression levels; S1 showed a higher expression of HYP-modified Col3a1 compared to S3 (Figure 4H). Example images and quantification of individual fibrillar collagen peptides showed specific spatial localization within the tumor microenvironment compared with surrounding pathologies (Figure 4I–L). Within a subtype, spatial expression pattern extended beyond the tumor region. However, S1 showed intense expression largely within the tumor, whereas S3 showed expression outside of the tumor within regions of bridging fibrosis. A major finding was that the proteomic data reported that not all HYP sites were modified. For instance, Col1a1 amino acid domain no. 914–925 was reported as the major modified isoform GPAGRP(1)GEVGP(0.017)P(0.983) (Figure 4J). This demonstrated that although four prolines were present, only two were earmarked as hydroxylated by sequencing. This sequence was increased in S1 tumors and lower in adjacent tissue, while S3 tumors showed low tumor expression with higher intensity in bridging fibrosis. Expression localization differences and site variability of hydroxylated proline modifications in fibrillar collagen may thus be defining factors in hepatocellular carcinoma tumors of poor outcomes.

Figure 4 Evaluation of tumor fibrillar collagen domains contributing to outcomes. (A) Image data analysis workflow. Peptides represented are those with highly confident identifications based on mass accuracy by sequencing analysis of the same tissue. (B) Heatmap of all unmodified peptides by mass-to-charge. (C) Heatmap of collagen hydroxyproline (HYP)-modified peptides by mass-to-charge. (D) Principal component analysis of unmodified peptides only demonstrates little separation by HCC subtype. (E) Principal component analysis of HYP peptides shows clear separation by HCC subtype. (F) Summary of unmodified and HYP-modified peptide intensity from all matched Col1a1 peptides extracted from tumors. Col1a1 demonstrates significant differences in unmodified versus modified S3 tumors. G) Summary of unmodified and HYP-modified peptides from all matched Col1a2 peptides extracted from tumors. Col1a2 demonstrates significant differences in unmodified versus modified for S3 tumors. (H) Lower levels of unmodified Col3a1 peptides were detected from S1 and S3 tumors compared to HYP-modified peptide levels. S1 tumors showed higher levels of HYP-modified Col3a1 peptides when compared to S3 tumors. (I) Hematoxylin and eosin staining of example tumors. Data were extracted for quantification from tumor regions outlined in red. (J) Example HYP-modified Col1a1 peptide (m/z = 1122.554). Intensity is elevated in S1 tumors when compared to S3 tumors. Expression expands throughout the tissue, appearing within S3 bridging fibrosis with a high intensity. (K) Example HYP-modified Col1a2 peptide (m/z = 1398.722) with low S3 tumor expression and appearing within intratumor fibrosis of S3 tumors. (L) Example Col3a1 peptide with overall decreased expression in S3 tissues and higher levels within S1 tumors. Exact p-values are from the Mann–Whitney U test.

Spatially Defined Peptides Show Predictive Value that Differentiates Genetic Subtypes

Since numerous reports indicate that the hepatic extracellular microenvironment is predictive of progression and outcomes, single peptides and combinations of multiple peptides were explored for their ability to distinguish between subtypes.69 Peptide intensities extracted from tumor image data, with sequence information blinded, were evaluated for the predictive value per peptide (Figure 5). With AUROC ≥ 0.75, single peptides showed significant ability to distinguish between groups. Examples of quantification by intensities and image patterns supported the high sensitivity and specificity of peptides derived from the tumor region of subtypes 1 and 3 (Figure 5 A–D). A random forest classifier was further used to evaluate the ability of single peptides to distinguish by subtype. This analysis showed single peptides with an area under the receiver operating curve (AUROC) ≥ 0.7 and a p-value ≤0.008 (Table S3). However, the majority of the single peptides showed high specificity (≥0.9; 48 peptides) and lower sensitivity (4 peptides >0.8). Additional random forest classifiers explored combinations of multiple peptides (Figure 6). This exploratory data set reported increasing distinguishment between groups based on signatures of four to six peptides. This analysis demonstrates the potential to develop strong predictive signatures derived from the extracellular microenvironment that differentiate hepatocellular carcinoma by outcomes.

Figure 5 Example peptides distinguishing S1 and S3 tumors include collagen-type differences and post-translational modifications of hydroxylated proline. ( A) Example Col1a1 peptide with 3/3 of prolines hydroxylated. This peptide is from the triple helical domain region of amino acids 404–415. (B) Example Col1a2 with 2/2 unmodified prolines and differentiating between S1 and S3. Expression is mapped to intratumor fibrosis (S1) and bridging fibrosis (S3). (C) Example Col3a1 peptide with 3/4 prolines hydroxylated. Spatial expression is detected in S1 tumors and decreased in S3 tumors. (D) Peptide domain from Col6a3 with no modifications and no prolines. Expression is overall absent within the S1 tumor microenvironment and detected within the S3 tumor microenvironment and bridging fibrosis. The p-values for AUC of representative peptides were produced using the Wilson/Brown method. Mann–Whitney Up-values are reported. AA – amino acid; ppm – parts per million.

Figure 6 Exploratory machine learning analysis. (A)–(C) Random forest machine learning algorithms were used to explore combinations of 4, 5, or 6 peptides from imaging data for potential predictive value of subtypes. (D) Summary of figure of merit for each peptide combination. AUROC = area under the receiver operating curve; SE = standard error; 95% CI– 95% confidence interval, PPV – positive predictive value, NPV – negative predictive value. Peptides are listed by the m/z value.

Discussion

In HCC, ECM deposition and remodeling are driven by WNT/TGFβ pathways at the proteomic or transcriptomic levels, which drive outcomes.12,18,22,70 In these studies, tumors with canonical WNT/TGFβ pathways represented subtype 1 with lower survival (40% 5-year survival rate),23 while subtype 3 showed liver-specific WNT pathways, somatic mutations in CTNNB and better survival (>80% 5-year survival rate). At the proteomic level, intratumor fibrosis driven by WNT/TGFβ signatures showed unique ECM profiles, illustrating that ECM has the potential in stratifying HCC subtypes by outcome.70 In the current study, we used a bottom-up proteomic imaging approach to investigate tumor resections previously characterized by genetic subtype12 and glycomics imaging.37 This bottom-up imaging strategy targets collagens, as well as other ECM proteins. A significant advancement in the current study is the examination of collagen structure regulation at the domain level, where cellular interaction occurs rather than at the single entity level where domain variation may be averaged out. HYP peptide domains from Col1a1, Col1a2, and Col3a1 differentiated between subtypes, with S1 having a consistently higher expression of HYP-modified levels. Previous works have noted that hydroxyprolines are accurate indicators of the level of fibrosis throughout the entire liver.71,72 Collagen modification of proline to hydroxyproline occurs through the action of prolyl hydroxylases, providing stabilization to the helix structure73 and altering sites of cell binding.36,74,75 Data from the present study support hydroxyproline site variation as an indicator of disease subtype, with many of the putatively identified peptides significantly upregulated in S1 tumors being modified with hydroxyprolines. Known interactions with Col1a1 motifs that require HYP include integrin binding and discoidin domain receptor binding,57,58,60,62 which promote HCC cell signaling, proliferation, and metastasis.55

Elevated levels of fibrillar collagen deposition have been linked to the aggressive nature of HCC progression,76 and this study reinforces this concept at the translational level. The deposition of collagens type I and III has been shown to increase with the progression of fibrosis towards cirrhosis due to the activity of hepatic stellate cells (HSCs).77,78 Col1a1 was previously found to be differentially upregulated in HCC tumors compared to normal tissues and promoted tumor cell survival and reproduction.29 Col1a1 is additionally a prognostic biomarker for poor outcomes in other cancers, including lung,79 gastric,80 and colorectal81 cancers. Our results indicate this association with potential outcomes, as we observed alterations in Col1a1 from tumors with elevated intensities in S1 subtypes (associated with poorer outcomes) compared to the S3 subtype (associated with better outcomes). Of note is that the identified Col1a1 peptide signatures in this study were associated with single to multiple hydroxyproline variations, which may contribute to their involvement in disease outcomes associated with HCC tumor tissues. Col1a1 has previously been identified as a potential target for therapeutics for HCC as its inhibition allowed more successful response to treatment.29,82 Col3a1 is present in all liver tissue, increases through liver fibrosis progression to cirrhosis, and changes Type I fibril formation in mouse Col3a1 knockouts.83 Recent work in breast cancer has shown that Col3a1 controls tumor dormancy, supporting a quiescent extracellular microenvironment that becomes proliferative when Col3a1 production is halted.84 The literature surrounding hepatocellular carcinoma is somewhat contradictory, with lower collagen type III found in HCC tumors, potentially due to degradation, while more recent reports highlight increases in collagen type III as having diagnostic potential.30 It is likely that significant domain variation and post-translational modifications, as well as HCC subtype, determine the role of specific collagens throughout the course of the disease. This is supported by recent work showing that serum levels of propeptide of type III collagen from patients with advanced HCC represent an independent prognostic factor, predicting cumulative liver-related clinical events including incidence and outcomes.85

Within the extracellular protein network, fibrinogen chain peptides were found to increase in S1 tumors compared to S3 tumors by both imaging data and LC-MS/MS data. Fibrinogen is a glycoprotein hexamer primarily secreted in the liver, leading to high levels detected in plasma.86 Its primary functions include hemostasis and wound healing by stabilizing blood clots. Other functions result from fibrinogen response to the microenvironment through many binding partners, particularly with integrins and receptors on cells associated with inflammation.87,88 In addition to its involvement in inflammatory response, elevated serum fibrinogen levels have been linked to worse prognosis in cancers, such as such as metastatic breast cancer,89 lung cancer,90 and hepatocellular carcinoma (HCC).65,66,91 Meta-analyses linking elevated serum fibrinogen in HCC patients to worse prognosis and in vitro studies showing elevated fibrinogen expression in HCC cells92 and HCC cell adherence to fibrinogen,93 have led to speculation of increased fibrinogen deposition by tumors. In the current study, imaging data demonstrated that fibrinogen peptides were detected in the HCC tumors, with minimal detection in the bridging fibrosis of S3 subtypes. Furthermore, peptides from the fibrinogen alpha chain and fibrinogen beta chain demonstrated complementary, non-overlapping expression patterns across both tissue types. Whether these discrete expression patterns are expressed by specific cellular phenotypes remains a work in progress. However, the data support that fibrinogen has a complex distribution within the HCC microenvironment, with increased expression in S1 tumors associated with worse outcomes.

The study explored the use of peptides produced from the extracellular microenvironment as markers to distinguish outcomes. Remarkably, the study identified that single peptides extracted from localized tumor regions showed high potential for distinguishing between subtypes. The combination of these peptides achieved significant distinction (AUROC > 0.95) and accuracy (>0.9) between subtypes. Previous studies have shown that collagen types, considered as single entities, are predictive of HCC progression and outcomes. However, it is important to consider that certain collagen types, such as Col1a1 chain, appear in all organs, but it is the structural arrangement that confers the function and interaction with specific cell types. Structural arrangement is due to translational composition and post-translational modifications that provide exacting chemistries for site interaction with cellular proteins. We hypothesize that the collagen regulation detected in this study reflects a fundamental difference in collagen-type regulation at the post-translational level. The investigation of collagen domain regulation presents a powerful tool for prediction of subtypes and presents opportunities for patient stratification, improving therapeutic targets, and monitoring therapeutic efficacies.

Limitations

The current study is limited by its small cohort size, and ongoing studies are working to expand the number of samples. Ionization by MALDI is complementary to ESI. Complementary ionization and the use of a single tissue section resulted in the limited identification of all peptides found in the imaging data. Tests directed at distinguishing ECM signatures by subtype may show overfitting due to the small sample set. The small data set limited the inclusion of clinical characteristics in developing predictors of outcome.

Conclusion

Stroma deposition initiates liver disease, determines cancer risk, outcomes, drug resistance, and predicts progression, but there is limited data on the molecular composition of stroma within the tumor microenvironment. The current study leverages ECM-targeted proteomic imaging mass spectrometry assays, investigating collagen domain variation in a previously validated cohort of HCC outcomes. This study defines that spatial and post-translational regulation of collagen domains within the HCC tumor differs between the S1 and S3 subtypes. This study further shows that collagen domain signatures and other extracellular proteins may be strong predictors of the HCC patient outcome. In early liver disease, qualitative and comprehensive proteomic analyses have shown that the extracellular microenvironment contributes to a pro-tumor microenvironment. Current developments in our laboratory include serum assays that demonstrate multiplexed signatures from circulating extracellular components. Comparison of spatial origins within the tumor microenvironment to extracellular domains shed into circulation may facilitate earlier detection of HCC subtype, leading to improved patient management. Combined studies that focus on serum products in comparison to the spatial microenvironment have huge potential to uncover novel markers, improve therapeutic targeting, and uncover signaling mechanisms associated with the progression of HCC from early fibrosis, cirrhosis, tumor emergence, and metastasis.

Data Availability Statement

Raw data are available for private FTP download at: ftp://MSV000094645@massive.ucsd.edu

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00099.Figure S1. Pathologically annotated, hematoxylin and eosin-stained tissue sections and accompanying segmentation analyses; Figure S2. Annotated MS2 spectra of discoidin domain receptor- and integrin-αβ-binding domains; Figure S3. Annotated MS2 spectra of fibrinogen alpha and beta chain peptides; Figure S4. Intensity heatmap of fibrillar collagen peptides from mass spectrometry imaging data; Table S1. Quantification of pathological annotations for each tissue; Table S2. Complete mass spectrometry imaging data matched to reference library sequencing data; Table S3. Random forest machine learning algorithm data of mass spectrometry imaging m/z values with AUROC > 0.7(PDF)

Supplementary Material

pr4c00099_si_001.pdf

Author Contributions

¶ J.M. and H.B.T. contributed equally to this work.

The authors declare the following competing financial interest(s): PMA serves as Advisory for GlycoPath and is shareholder for GlycoPath and N-zyme Scientifics. RRD serves as Advisory for GlycoPath and is shareholder for GlycoPath and N-zyme Scientifics. ASM serves as Advisory for GlycoPath, GlycoTest and is shareholder for GlycoPath, GlycoTest and N-zyme Scientifics. YH serves as Advisory for Helio Genomics, Alentis Therapeutics, Espervita Therapeutics, Roche Diagnostics, Elevar Therapeutics and is shareholder in Alentis Therapeutics, Espervita Therapeutics.

Acknowledgments

J.M. was supported in part by the Cellular, Biochemical and Molecular Sciences Training Program 5T32GM132055 (NIH/NIGMS) and NIH/NCI R21CA263464. P.M.A. was supported by NIH/NCI R21CA263464, NIH/NCI R01CA253460; P20GM103542 (NIH/NIGMS) and in part by Hollings Cancer Center Support Grant P30 CA138313 at the Medical University of South Carolina. A.S.M., P.M.A., and R.R.D. were supported by Team Science Award through pilot research funding, Hollings Cancer Center’s Cancer Center Support Grant P30 CA138313 at the Medical University of South Carolina. Y.H. was supported in part by the Cancer Prevention & Research Institute of Texas (CPRIT;RR180016) and by NIH/NCI R01CA233794, R01CA282178, R01CA255621, U01CA288375, and U01CA283935. Work supported in part by NIH/NIDDK P30DK123704, NIH/NCI P30CA138313. The Mass Spectrometry Facility and Redox Proteomics Core are supported by the University and P20GM103542 (NIH/NIGMS) with shared instrumentation (NIH/OD) S10 OD010731 & S10 OD025126 to LEB and S10 0D030212 to PMA. The contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH or NCATS.

Abbreviations

HCC hepatocellular carcinoma

S1–3 Subtypes 1–3

LC-MS/MS liquid chromatography tandem mass spectrometry

MSI mass spectrometry imaging

DDR discoidin domain receptor

HYP hydroxylated proline

ColXaY alpha chain Y of collagen-type X

AUROC area under the receiver operating curve

HCV hepatitis C virus

HBV hepatitis B virus

NAFLD nonalcoholic fatty liver disease

MRI magnetic resonance imaging

CT computed tomography

miRNA micro ribonucleic acid

mRNA messenger ribonucleic acid

TGFβ1 transforming growth factor beta 1

Wnt wingless-related integration site

SHH sonic hedgehog

CTNNB catenin beta

HSC hepatic stellate cells

PTM post-translational modification

PH prolyl hydroxylase

FFPE formalin-fixed paraffin-embedded

CHCA α-cyano-4-hydroxycinnamic acid

MALDI matrix-assisted laser/desorption ionization

QTOF quadrupole time-of-flight

TIC total ion current

HPLC high-performance liquid chromatography

HCD higher-energy collisional dissociation

ECM-MSI extracellular matrix-mass spectrometry imaging

H&E hematoxylin and eosin

ppm parts per million

FGA fibrinogen alpha chain

FGB fibrinogen beta chain

PCA principal component analysis

ESI electrospray ionization
==== Refs
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