Proteomic identification of biomarkers of traumatic brain injury

Traumatic brain injury (TBI) is a major national health problem without a US Food and Drug Administration-approved therapy. This review summarizes the importance of discovering relevant TBI protein biomarkers and presents logical rationale that neuroproteomic technologies are uniquely suited for the discovery of otherwise unnoticed TBI biomarkers. It highlights that one must make careful decisions when choosing which paradigm (human vs. animal models) and which biologic samples to use for such proteomic studies. It further outlines some of the desirable attributes of an ideal TBI biomarker and discusses how biomarkers discovered proteomically are complementary to those identified by traditional approaches. Lastly, the most important sequela of any proteomically identified TBI biomarker is validation in preclinical or clinical samples.     

Biomarkers of human skin cells identified using DermArray DNA arrays and new bioinformatics methods

Biomarker genes of human skin-derived cells were identified by new simple bioinformatic methods and DNA microarray analysis utilizing in vitro cultures of normal neonatal human epidermal keratinocytes, melanocytes, and dermal fibroblasts. A survey of 4405 human cDNAs was performed using DermArray DNA microarrays. Biomarkers were rank ordered by "likelihood ratio" algorithms and stringent selection criteria that have general applicability for analyzing a minimum of three RNA samples. Signature biomarker genes (up-regulated in one cell type) and anti-signature biomarker genes (down-regulated in one cell type) were determined for the three major skin cell types. Many of the signature genes are known biomarkers for these cell types. In addition, 17 signature genes were identified as ESTs, and 22 anti-signature biomarkers were discovered. Quantitative RT-PCR was used to verify nine signature biomarker genes. A total of 158 biomarkers of normal human skin cells were identified, many of which may be valuable in diagnostic applications and as molecular targets for drug discovery and therapeutic intervention.     

Plasma Soluble Prion Protein,a Potential Biomarker for Sport-Related Concussions: A Pilot Study

Sport-related mild traumatic brain injury (mTBI) or concussion is a significant health concern to athletes with potential long-term consequences. The diagnosis of sport concussion and return to sport decision making is one of the greatest challenges facing health care clinicians working in sports. Blood biomarkers have recently demonstrated their potential in assisting the detection of brain injury particularly, in those cases with no obvious physical injury. We have recently discovered plasma soluble cellular prion protein (PrP^C) as a potential reliable biomarker for blast induced TBI (bTBI) in a rodent animal model. In order to explore the application of this novel TBI biomarker to sport-related concussion, we conducted a pilot study at the University of Saskatchewan (U of S) by recruiting athlete and non-athlete 18 to 30 year-old students. Using a modified quantitative ELISA method, we first established normal values for the plasma soluble PrP^C in male and female students. The measured plasma soluble PrP^C in confirmed concussion cases demonstrated a significant elevation of this analyte in post-concussion samples. Data collected from our pilot study indicates that the plasma soluble PrP^C is a potential biomarker for sport-related concussion, which may be further developed into a clinical diagnostic tool to assist clinicians in the assessment of sport concussion and return-to-play decision making.     

Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer

In recent years, Prostate Specific Antigen (PSA) testing is widespread and has been associated with deceased mortality rates; however, this testing has raised concerns of overdiagnosis and overtreatment. It is clear that additional biomarkers are required. To identify these biomarkers, we have undertaken proteomics and metabolomics expression profiles of serum samples from BPH, Gleason score 5 and 7 using two-dimensional difference in gel electrophoresis (2D-DIGE) and nuclear magnetic resonance spectroscopy (NMR). Panels of serum protein biomarkers were identified by applying Random Forests to the 2D-DIGE data. The evaluation of selected biomarker panels has shown that they can provide higher prediction accuracy than the current diagnostic standard. With careful validation of these serum biomarker panels, these panels may potentially help to reduce unnecessary invasive diagnostic procedures and more accurately direct the urologist to curative surgery.     

Proteomic analysis of traumatic brain injury: the search for biomarkers

Although there are a number of causes of traumatic brain injury (TBI), the armed conflict in Iraq and Afghanistan has brought this disorder to the attention of the global community. A biomarker that would enable army medics to rapidly diagnose the severity of TBI on the battle-field would be a huge asset. Unfortunately, the study of TBI has not historically attracted the proteomic research community’s interest as other disorders have, such as cancer. On the positive side, however, many of the analytical and technological challenges that were overcome in the development of biofluid proteomic methods are now being applied to the study of TBI. In this review, we discuss and highlight select examples of discovery-driven proteomic studies focused on finding effective biomarkers for TBI.     

Proteomic analysis of traumatic brain injury: the search for biomarkers

Although there are a number of causes of traumatic brain injury (TBI), the armed conflict in Iraq and Afghanistan has brought this disorder to the attention of the global community. A biomarker that would enable army medics to rapidly diagnose the severity of TBI on the battle-field would be a huge asset. Unfortunately, the study of TBI has not historically attracted the proteomic research community's interest as other disorders have, such as cancer. On the positive side, however, many of the analytical and technological challenges that were overcome in the development of biofluid proteomic methods are now being applied to the study of TBI. In this review, we discuss and highlight select examples of discovery-driven proteomic studies focused on finding effective biomarkers for TBI.     

Novel serological biomarker panel using protein microarray can distinguish active TB from latent TB infection

BackgroundRapid laboratory technologies which can effectively distinguish active tuberculosis (ATB) from controls and latent tuberculosis infection (LTBI) are lacked.The objective of this study is to explore MTB biomarkers in serum that can distinguish ATB from LTBI.MethodsWe constructed a tuberculosis protein microarray containing 64 MTB associated antigens. We then used this microarray to screen 180 serum samples, from patients with ATB and LTBI, and healthy volunteer controls. Both SAM (Significance analysis of microarrays) and ROC curve analysis were used to identify the differentially recognized biomarkers between groups. Extra 300 serum samples from patients with ATB and LTBI, and healthy volunteer controls were employed to validate the identified biomarkers using ELISA-based method.ResultsAccording to the results, the best biomarker combinations of 4 proteins (Rv1860, RV3881c, Rv2031c and Rv3803c) were selected. The biomarker panel containing these 4 proteins has reached a sensitivity of 93.3% and specificity of 97.7% for distinguishing ATB from LTBI, and a sensitivity of 86% and specificity of 97.6% for distinguishing ATB from HC.ConclusionThe biomarker combination in this study has high sensitivity and specificity in distinguishing ATB from LTBI, suggesting it is worthy for further validation in more clinical samples.     

A comprehensive peptidome profiling technology for the identification of early detection biomarkers for lung adenocarcinoma

The mass spectrometry-based peptidomics approaches have proven its usefulness in several areas such as the discovery of physiologically active peptides or biomarker candidates derived from various biological fluids including blood and cerebrospinal fluid. However, to identify biomarkers that are reproducible and clinically applicable, development of a novel technology, which enables rapid, sensitive, and quantitative analysis using hundreds of clinical specimens, has been eagerly awaited. Here we report an integrative peptidomic approach for identification of lung cancer-specific serum peptide biomarkers. It is based on the one-step effective enrichment of peptidome fractions (molecular weight of 1,000-5,000) with size exclusion chromatography in combination with the precise label-free quantification analysis of nano-LC/MS/MS data set using Expressionist proteome server platform. We applied this method to 92 serum samples well-managed with our SOP (standard operating procedure) (30 healthy controls and 62 lung adenocarcinoma patients), and quantitatively assessed the detected 3,537 peptide signals. Among them, 118 peptides showed significantly altered serum levels between the control and lung cancer groups (p<0.01 and fold change >5.0). Subsequently we identified peptide sequences by MS/MS analysis and further assessed the reproducibility of Expressionist-based quantification results and their diagnostic powers by MRM-based relative-quantification analysis for 96 independently prepared serum samples and found that APOA4 273-283, FIBA 5-16, and LBN 306-313 should be clinically useful biomarkers for both early detection and tumor staging of lung cancer. Our peptidome profiling technology can provide simple, high-throughput, and reliable quantification of a large number of clinical samples, which is applicable for diverse peptidome-targeting biomarker discoveries using any types of biological specimens.     

A tool for biomarker discovery in the urinary proteome: a manually curated human and animal urine protein biomarker database

Urine is an important source of biomarkers. A single proteomics assay can identify hundreds of differentially expressed proteins between disease and control samples; however, the ability to select biomarker candidates with the most promise for further validation study remains difficult. A bioinformatics tool that allows accurate and convenient comparison of all of the existing related studies can markedly aid the development of this area. In this study, we constructed the Urinary Protein Biomarker (UPB) database to collect existing studies of urinary protein biomarkers from published literature. To ensure the quality of data collection, all literature was manually curated. The website (http://122.70.220.102/biomarker) allows users to browse the database by disease categories and search by protein IDs in bulk. Researchers can easily determine whether a biomarker candidate has already been identified by another group for the same disease or for other diseases, which allows for the confidence and disease specificity of their biomarker candidate to be evaluated. Additionally, the pathophysiological processes of the diseases can be studied using our database with the hypothesis that diseases that share biomarkers may have the same pathophysiological processes. Because of the natural relationship between urinary proteins and the urinary system, this database may be especially suitable for studying the pathogenesis of urological diseases. Currently, the database contains 553 and 275 records compiled from 174 and 31 publications of human and animal studies, respectively. We found that biomarkers identified by different proteomic methods had a poor overlap with each other. The differences between sample preparation and separation methods, mass spectrometers, and data analysis algorithms may be influencing factors. Biomarkers identified from animal models also overlapped poorly with those from human samples, but the overlap rate was not lower than that of human proteomics studies. Therefore, it is not clear how well the animal models mimic human diseases.     

A lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform for identification of multiple liver cancer biomarkers in human plasma

Aberrantly glycosylated proteins related to liver cancer progression were captured with specific lectin and identified from human plasma by multiple reaction monitoring (MRM) mass spectrometry as multiple biomarkers for hepatocellular carcinoma (HCC). The lectin fractionation for fucosylated protein glycoforms in human plasma was conducted with a fucose-specific aleuria aurantia lectin (AAL). Following tryptic digestion of the lectin-captured fraction, plasma samples from 30 control cases (including 10 healthy, 10 hepatitis B virus [HBV], and 10 cirrhosis cases) and 10 HCC cases were quantitatively analyzed by MRM to identify which glycoproteins are viable HCC biomarkers. A1AG1, AACT, A1AT, and CERU were found to be potent biomarkers to differentiate HCC plasma from control plasmas. The AUROC generated independently from these four biomarker candidates ranged from 0.73 to 0.92. However, the lectin-coupled MRM assay with multiple combinations of biomarker candidates is superior statistically to those generated from the individual candidates with AUROC more than 0.95, which can be an alternative to the immunoassay inevitably requiring tedious development of multiple antibodies against biomarker candidates to be verified. Eventually the lectin-coupled, targeted proteomic mass spectrometry (MRM MS) platform was found to be efficient to identify multiple biomarkers from human plasma according to cancer progression.     

Characterization of the human gastric fluid proteome reveals distinct pH-dependent protein profiles: implications for biomarker studies

Gastric fluid is a source of gastric cancer biomarkers. However, very little is known about the normal gastric fluid proteome and its biological variations. In this study, we performed a comprehensive analysis of the human gastric fluid proteome using samples obtained from individuals with benign gastric conditions. Gastric fluid proteins were prefractionated using ultracentrifuge filters (3 kDa cutoff) and analyzed by two-dimensional gel electrophoresis (2-DE) and multidimensional LC-MS/MS. Our 2-DE analysis of 170 gastric fluid samples revealed distinct protein profiles for acidic and neutral samples, highlighting pH effects on protein composition. By 2D LC-MS/MS analysis of pooled samples, we identified 284 and 347 proteins in acidic and neutral samples respectively (FDR ≤1%), of which 265 proteins (72.4%) overlapped. However, unlike neutral samples, most proteins in acidic samples were identified from peptides in the filtrate (i.e., <3 kDa). Consistent with this finding, immunoblot analysis of six potential gastric cancer biomarkers rarely detected full-length proteins in acidic samples. These findings have important implications for biomarker studies because a majority of gastric cancer patients have neutral gastric fluid compared to noncancer controls. Consequently, sample stratification, choice of proteomic approaches, and validation strategy can profoundly affect the interpretation of biomarker findings. These observations should help to refine gastric fluid biomarker studies.     

Sub-speciating Campylobacter jejuni by proteomic analysis of its protein biomarkers and their post-translational modifications

We have identified several protein biomarkers of three Campylobacter jejuni strains (RM1221, RM1859, and RM3782) by proteomic techniques. The protein biomarkers identified are prominently observed in the time-of-flight mass spectra (TOF MS) of bacterial cell lysate supernatants ionized by matrix-assisted laser desorption/ionization (MALDI). The protein biomarkers identified were: DNA-binding protein HU, translation initiation factor IF-1, cytochrome c553, a transthyretin-like periplasmic protein, chaperonin GroES, thioredoxin Trx, and ribosomal proteins: L7/L12 (50S), L24 (50S), S16 (30S), L29 (50S), and S15 (30S), and conserved proteins similar to strain NCTC 11168 proteins Cj1164 and Cj1225. The protein biomarkers identified appear to represent high copy, intact proteins. The significant findings are as follows: (1) Biomarker mass shifts between these strains were due to amino acid substitutions of the primary polypeptide sequence and not due to changes in post-translational modifications (PTMs). (2) If present, a PTM of a protein biomarker appeared consistently for all three strains, which supported that the biomarker mass shifts observed between strains were not due to PTM variability. (3) The PTMs observed included N-terminal methionine (N-Met) cleavage as well as a number of other PTMs. (4) It was discovered that protein biomarkers of C. jejuni (as well as other thermophilic Campylobacters) appear to violate the N-Met cleavage rule of bacterial proteins, which predicts N-Met cleavage if the penultimate residue is threonine. Two protein biomarkers (HU and 30S ribosomal protein S16) that have a penultimate threonine residue do not show N-Met cleavage. In all other cases, the rule correctly predicted N-Met cleavage among the biomarkers analyzed. This exception to the N-Met cleavage rule has implications for the development of bioinformatics algorithms for protein/pathogen identification. (5) There were fewer biomarker mass shifts between strains RM1221 and RM1859 compared to strain RM3782. As the mass shifts were due to the frequency of amino acid substitutions (and thus underlying genetic variations), this suggested that strains RM1221 and RM1859 were phylogenetically closer to one another than to strain RM3782 (in addition, a protein biomarker prominent in the spectra of RM1221 and RM1859 was absent from the RM3782 spectrum due to a nonsense mutation in the gene of the biomarker). These observations were confirmed by a nitrate reduction test, which showed that RM1221 and RM1859 were C. jejuni subsp. jejuni whereas RM3782 was C. jejuni subsp. doylei. This result suggests that detection/identification of protein biomarkers by pattern recognition and/or bioinformatics algorithms may easily subspeciate bacterial microorganisms. (6) Finally, the number and variation of PTMs detected in this relatively small number of protein biomarkers suggest that bioinformatics algorithms for pathogen identification may need to incorporate many more possible PTMs than suggested previously in the literature.     

Cancer-associated carbohydrate antigens as potential biomarkers for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common human malignancies. Therefore, developing the early, high-sensitivity diagnostic biomarkers to prevent HCC is urgently needed. Serum a-fetoprotein (AFP), the clinical biomarker in current use, is elevated in only ~60% of patients with HCC; therefore, identification of additional biomarkers is expected to have a significant impact on public health. In this study, we used glycan microarray analysis to explore the potential diagnostic value of several cancer-associated carbohydrate antigens (CACAs) as biomarkers for HCC. We used glycan microarray analysis with 58 different glycan analogs for quantitative comparison of 593 human serum samples (293 HCC samples; 133 chronic hepatitis B virus (HBV) infection samples, 134 chronic hepatitis C virus (HCV) infection samples, and 33 healthy donor samples) to explore the diagnostic possibility of serum antibody changes as biomarkers for HCC. Serum concentrations of anti-disialosyl galactosyl globoside (DSGG), anti-fucosyl GM1 and anti-Gb2 were significantly higher in patients with HCC than in chronic HBV infection individuals not in chronic HCV infection patients. Overall, in our study population, the biomarker candidates DSGG, fucosyl GM1 and Gb2 of CACAs achieved better predictive sensitivity than AFP. We identified potential biomarkers suitable for early detection of HCC. Glycan microarray analysis provides a powerful tool for high-sensitivity and high-throughput detection of serum antibodies against CACAs, which may be valuable serum biomarkers for the early detection of persons at high risk for HCC.     

Candidate proteomic biomarkers for non-alcoholic fatty liver disease (steatosis and non-alcoholic steatohepatitis) discovered with mass-spectrometry: a systematic review

Context: Non-alcoholic fatty liver disease (NAFLD) is characterized by lipid accumulation in the liver which is accompanied by a series of metabolic deregulations. There are sustained research efforts focusing upon biomarker discovery for NAFLD diagnosis and its prognosis in order investigate and follow-up patients as minimally invasive as possible.

Objective: The objective of this study is to critically review proteomic studies that used mass spectrometry techniques and summarize relevant proteomic NAFLD candidate biomarkers.

Methods: Medline and Embase databases were searched from inception to December 2014.

Results: A final number of 22 records were included that identified 251 candidate proteomic biomarkers. Thirty-three biomarkers were confirmed – 14 were found in liver samples, 21 in serum samples, and two from both serum and liver samples.

Conclusion: Some of the biomarkers identified have already been extensively studied regarding their diagnostic and prognostic capacity. However, there are also more potential biomarkers that still need to be addressed in future studies.     

Increased CSF Levels of Phosphorylated Neurofilament Heavy Protein following Bout in Amateur Boxers

Introduction

Diagnosis of mild TBI is hampered by the lack of imaging or biochemical measurements for identifying or quantifying mild TBI in a clinical setting. We have previously shown increased biomarker levels of protein reflecting axonal (neurofilament light protein and tau) and glial (GFAP and S-100B) damage in cerebrospinal fluid (CSF) after a boxing bout. The aims of this study were to find other biomarkers of mild TBI, which may help clinicians diagnose and monitor mild TBI, and to calculate the role of APOE ε4 allele genotype which has been associated with poor outcome after TBI.

Materials and Methods

Thirty amateur boxers with a minimum of 45 bouts and 25 non-boxing matched controls were included in a prospective cohort study. CSF and blood were collected at one occasion between 1 and 6 days after a bout, and after a rest period for at least 14 days (follow up). The controls were tested once. CSF levels of neurofilament heavy (pNFH), amyloid precursor proteins (sAPPα and sAPPβ), ApoE and ApoA1 were analyzed. In blood, plasma levels of Aβ42 and ApoE genotype were analyzed.

Results

CSF levels of pNFH were significantly increased between 1 and 6 days after boxing as compared with controls (p<0.001). The concentrations decreased at follow up but were still significantly increased compared to controls (p = 0.018). CSF pNFH concentrations correlated with NFL (r =  0.57 after bout and 0.64 at follow up, p<0.001). No significant change was found in the other biomarkers, as compared to controls. Boxers carrying the APOE ε4 allele had similar biomarker concentrations as non-carriers.

Conclusions

Subconcussive repetitive trauma in amateur boxing causes a mild TBI that may be diagnosed by CSF analysis of pNFH, even without unconsciousness or concussion symptoms. Possession of the APOE ε4 allele was not found to influence biomarker levels after acute TBI.     

Biomarkers of human cutaneous squamous cell carcinoma from tissues and cell lines identified by DNA microarrays and qRT-PCR

Squamous cell carcinoma (SCC) is the second most common form of skin cancer in Caucasians. Here we report on the identification of biomarkers of human cutaneous SCC cell lines in vitro and tissue samples in vivo using DermArray and PharmArray DNA microarrays, consisting of ca. 7400 unique human cDNAs. Differentially expressed genes were identified in two facial skin SCC cell lines (SCC 12 and SCC 13) compared to normal keratinocytes, and three cutaneous SCC tissue samples compared to normal skin. Quantitative validations of up- and down-regulated biomarkers were performed by qRT-PCR on 23 biomarker genes for the cell lines and 20 biomarker genes for the tumor tissues. In addition, three oral SCC cell lines were also included in the qRT-PCR validations for comparison, and the biomarker profiles were highly similar between the cutaneous and the oral SCC cell lines for all 23 biomarkers examined. The expression profiles for a variety of non-cutaneous SCC types, such as head-and-neck, oral, and lung, have been previously published. This report is the first to describe biomarkers for cutaneous SCC in two contexts, in vitro and in vivo. Although there was minimal overlap between the two different contexts using DNA microarrays, five genes were found common to both the cell lines and tissues, namely fibronectin 1, annexin A5, glyceraldehyde 3-phosphate dehydrogenase, zinc-finger protein 254, and huntingtin-associated protein interacting protein. Some of our previously published biomarkers of normal keratinocytes were down-regulated in SCC, suggestive of the dedifferentiated status of the transformed cells. While recent reports have identified some of the same genes as SCC biomarkers, for instance in head-and-neck cancer, thereby validating our approach, we have identified some novel biomarkers for cutaneous disease. These biomarker lists may be useful in molecular diagnostics of non-melanoma skin cancer, and a subset of the biomarkers might serve as suitable targets for drug discovery efforts of therapies for SCC.     

A targeted proteomics-based pipeline for verification of biomarkers in plasma

High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.     

Targeted Lipid Profiling Discovers Plasma Biomarkers of Acute Brain Injury

Prior efforts to identify a blood biomarker of brain injury have relied almost exclusively on proteins; however their low levels at early time points and poor correlation with injury severity have been limiting. Lipids, on the other hand, are the most abundant molecules in the brain and readily cross the blood-brain barrier. We previously showed that certain sphingolipid (SL) species are highly specific to the brain. Here we examined the feasibility of using SLs as biomarkers for acute brain injury. A rat model of traumatic brain injury (TBI) and a mouse model of stroke were used to identify candidate SL species though our mass-spectrometry based lipid profiling approach. Plasma samples collected after TBI in the rat showed large increases in many circulating SLs following injury, and larger lesions produced proportionately larger increases. Plasma samples collected 24 hours after stroke in mice similarly revealed a large increase in many SLs. We constructed an SL score (sum of the two SL species showing the largest relative increases in the mouse stroke model) and then evaluated the diagnostic value of this score on a small sample of patients (n = 14) who presented with acute stroke symptoms. Patients with true stroke had significantly higher SL scores than patients found to have non-stroke causes of their symptoms. The SL score correlated with the volume of ischemic brain tissue. These results demonstrate the feasibility of using lipid biomarkers to diagnose brain injury. Future studies will be needed to further characterize the diagnostic utility of this approach and to transition to an assay method applicable to clinical settings.     

Low abundance protein enrichment for discovery of candidate plasma protein biomarkers for early detection of breast cancer

Molecular biomarkers of early stage breast cancer may improve the sensitivity and specificity of diagnosis. Plasma biomarkers have additional value in that they can be monitored with minimal invasiveness. Plasma biomarker discovery by genome-wide proteomic methods is impeded by the wide dynamic range of protein abundance and the heterogeneity of protein expression in healthy and disease populations which requires the analysis of a large number of samples. We addressed these issues through the development of a novel protocol that couples a combinatorial peptide ligand library protein enrichment strategy with isobaric label-based 2D LC-MS/MS for the identification of candidate biomarkers in high throughput. Plasma was collected from patients with stage I breast cancer or benign breast lesions. Low abundance proteins were enriched using a bead-based combinatorial library of hexapeptides. This resulted in the identification of 397 proteins, 22% of which are novel plasma proteins. Twenty-three differentially expressed plasma proteins were identified, demonstrating the effectiveness of the described protocol and defining a set of candidate biomarkers to be validated in independent samples. This work can be used as the basis for the design of properly powered investigations of plasma protein expression for biomarker discovery in larger cohorts of patients with complex disease.     

Identification of a novel tumour microenvironment-based prognostic biomarker in skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is one of the most destructive skin malignancies and has attracted worldwide attention. However, there is a lack of prognostic biomarkers, especially tumour microenvironment (TME)-based prognostic biomarkers. Therefore, there is an urgent need to investigate the TME in SKCM, as well as to identify efficient biomarkers for the diagnosis and treatment of SKCM patients. A comprehensive analysis was performed using SKCM samples from The Cancer Genome Atlas and normal samples from Genotype-Tissue Expression. TME scores were calculated using the ESTIMATE algorithm, and differential TME scores and differentially expressed prognostic genes were successively identified. We further identified more reliable prognostic genes via least absolute shrinkage and selection operator regression analysis and constructed a prognostic prediction model to predict overall survival. Receiver operating characteristic analysis was used to evaluate the diagnostic efficacy, and Cox regression analysis was applied to explore the relationship with clinicopathological characteristics. Finally, we identified a novel prognostic biomarker and conducted a functional enrichment analysis. After considering ESTIMATEScore and tumour purity as differential TME scores, we identified 34 differentially expressed prognostic genes. Using least absolute shrinkage and selection operator regression, we identified seven potential prognostic biomarkers (SLC13A5, RBM24, IGHV3OR16-15, PRSS35, SLC7A10, IGHV1-69D and IGHV2-26). Combined with receiver operating characteristic and regression analyses, we determined PRSS35 as a novel TME-based prognostic biomarker in SKCM, and functional analysis enriched immune-related cells, functions and signalling pathways. Our study indicated that PRSS35 could act as a potential prognostic biomarker in SKCM by investigating the TME, so as to provide new ideas and insights for the clinical diagnosis and treatment of SKCM.