Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer

Nomogram to Predict Nodal Recurrence-Free Survival in Early Oral Squamous Cell Carcinoma

OBJECTIVE

This study aimed to develop and internally validate a prognostic nomogram for predicting nodal recurrence-free survival (NRFS) in patients with early-stage oral squamous cell carcinoma (OSCC) with clinically negative neck lymph nodes.

MATERIALS AND METHODS

The management of early-stage oral cancer patients with clinically negative neck lymph nodes (cN0) remains controversial, especially concerning the need for elective neck dissection. Data from a single institution spanning 2010 to 2020 were utilized to develop and evaluate the nomogram. The nomogram was constructed using multivariable Cox regression and LASSO regression analyses to identify independent risk factors for lymph node metastasis. Internal validation was performed using bootstrap resampling to assess the nomogram's predictive accuracy.

RESULTS

A total of 930 cN0 patients with T1 and T2 stage OSCC were randomly divided into training and validation cohorts (8:2 ratio). Independent risk factors for lymph node metastasis included tumor pathological grade (well: reference, moderate/poor: OR 1.69), cT (cT1: reference, cT2: OR 2.01), history of drinking (never: reference, current/former: OR 1.72), and depth of invasion (0 mm < DOI ≤ 5 mm: reference, 5 mm < DOI ≤ 10 mm: OR 1.31). The nomogram, incorporating these variables, demonstrated good predictive accuracy with a C-index of 0.67 (95% CI: 0.58-0.76) in the validation set. In both training and validation groups, the nomogram effectively stratified patients into low-risk and high-risk groups for occult cervical nodal metastases (p < 0.05).

CONCLUSIONS

The nomogram enables risk stratification and improved identification of occult cervical nodal metastases in clinically node-negative OSCC patients by incorporating tumor-specific and patient-specific risk factors.

Salivary CD44 and Total Protein Levels to Detect Risk for Oral and Oropharyngeal Cancer Recurrence: A Nonrandomized Clinical Trial

Importance

Oral and oropharyngeal cancer have low survival rates, and incidence continues to increase.

Objective

To determine whether soluble CD44 and total protein (TP) are useful for monitoring head and neck cancer recurrence, either used in a point-of-care (POC) test or as individual laboratory-based biomarkers.

Design, Setting, and Participants

This multi-institutional nonrandomized clinical trial testing a novel diagnostic/screening assay took place across the University of California, San Diego; Johns Hopkins University; the Greater Baltimore Medical Center; New York University; and the San Diego Veterans Affairs Hospital. Patients with newly biopsy-proven, untreated oral cavity and oropharyngeal cancer were enrolled. Patients were enrolled April 2017 to April 2019, and data were analyzed December 2022 to June 2023.

Exposure

POC salivary oral rinse test.

Main Outcomes and Measures

Oral rinses were collected at pretreatment baseline and 3, 6, 12, and 18 months after completion of therapy; participants were then followed up for 3 years to define disease status. Associations of baseline characteristics with a positive test were evaluated by Fisher exact test. The association of a positive value on the CD44 or TP test with progression-free survival was evaluated in an adjusted multivariable proportional hazards model.

Results

Of 172 patients enrolled, the mean (SD) age was 62.5 (10.2) years, and 122 (70.9%) identified as male. Additionally, 92 patients (53.3%) had never smoked, 99 (57.6%) formerly or currently drank alcohol, and 113 (65.7%) presented with oropharyngeal cancers, which were positive for human papillomavirus in 95 (84.1%). Tumor site was associated with test results at baseline; patients with oral cavity cancer had a higher baseline positive POC test rate (47 of 51 [92.2%]) compared to patients with oropharyngeal cancer (85 of 110 [77.3%]). Using Cox regression models with CD44 or TP level as a time-varying covariate, a higher CD44 level showed a statistically significant association with a higher hazard of recurrence (hazard ratio, 1.06; 95% CI, 1.00-1.12), though the TP level was not statistically significant. In multivariate adjusted analysis, higher CD44 and TP levels were associated with increased hazard ratios of recurrence of 1.13 (95% CI, 1.04-1.22) and 3.51 (95% CI, 1.24-9.98), respectively.

Conclusion and Relevance

In this multi-institutional nonrandomized clinical trial of an assay, posttreatment longitudinal monitoring for elevated salivary CD44 and TP levels using an enzyme-linked immunosorbent assay-based laboratory test identified patients at increased risk of future cancer recurrence. The CD44 and TP rapid POC test holds some promise, but further development is needed for this indication.

Trial Registration

ClinicalTrials.gov Identifier: NCT03148665.

Mediator complex subunit 1 promotes oral squamous cell carcinoma progression by activating MMP9 transcription and suppressing CD8+ T cell antitumor immunity

BACKGROUND

The role of Mediator complex subunit 1 (MED1), a pivotal transcriptional coactivator implicated in diverse biological pathways, remains unexplored in the context of oral squamous cell carcinoma (OSCC). This study aims to elucidate the contributory mechanisms and potential impact of MED1 on the progression of OSCC.

METHODS

The expression and clinical significance of MED1 in OSCC tissues were evaluated through the bioinformatics analyses. The effects of MED1 on the biological behavior of OSCC cancer cells were assessed both in vitro and in vivo. Dual-luciferase reporter assay, chromatin immunoprecipitation (ChIP) assay, bioinformatic analysis, CD8+ T cell isolation experiment, coculture experiment, enzyme-linked immunosorbent assay (ELISA), and flow cytometric analysis were employed to elucidate the underlying mechanism through which MED1 operates in the progression of OSCC.

RESULTS

MED1 exhibited upregulation in both OSCC tissues and multiple OSCC cell lines, which correlated with decreased overall survival in patients. In vitro experiments demonstrated that knockdown of MED1 in metastatic OSCC cell lines SCC-9 and UPCI-SCC-154 hindered cell migration and invasion, while overexpression of MED1 promoted these processes. Whereas, MED1 knockdown had no impact on proliferation of cell lines mentioned above. In vivo studies further revealed that downregulation of MED1 effectively suppressed distant metastasis in OSCC. Mechanistically, MED1 enhanced the binding of transcription factors c-Jun and c-Fos to the matrix metalloprotein 9 (MMP9) promoters, resulting in a significant upregulation of MMP9 transcription. This process contributes to the migration and invasion of SCC-9 and UPCI-SCC-154 cells. Furthermore, MED1 modulated the expression of programmed death-ligand 1 (PD-L1) through the Notch signaling pathway, consequently impacting the tumor-killing capacity of CD8+ T cells in the tumor microenvironment.

CONCLUSIONS

Our findings indicate that MED1 plays a pivotal role in OSCC progression through the activation of MMP9 transcription and suppression of CD8+ T cell antitumor immunity, suggesting that MED1 may serve as a novel prognostic marker and therapeutic target in OSCC.

Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology

OBJECTIVE

This study aimed to assess reporting quality of machine learning (ML) algorithms in the head and neck oncology literature using the TRIPOD-AI criteria.

DATA SOURCES

A comprehensive search was conducted using PubMed, Scopus, Embase, and Cochrane Database of Systematic Reviews, incorporating search terms related to "artificial intelligence," "machine learning," "deep learning," "neural network," and various head and neck neoplasms.

REVIEW METHODS

Two independent reviewers analyzed each published study for adherence to the 65-point TRIPOD-AI criteria. Items were classified as "Yes," "No," or "NA" for each publication. The proportion of studies satisfying each TRIPOD-AI criterion was calculated. Additionally, the evidence level for each study was evaluated independently by two reviewers using the Oxford Centre for Evidence-Based Medicine (OCEBM) Levels of Evidence. Discrepancies were reconciled through discussion until consensus was reached.

RESULTS

The study highlights the need for improvements in ML algorithm reporting in head and neck oncology. This includes more comprehensive descriptions of datasets, standardization of model performance reporting, and increased sharing of ML models, data, and code with the research community. Adoption of TRIPOD-AI is necessary for achieving standardized ML research reporting in head and neck oncology.

CONCLUSION

Current reporting of ML algorithms hinders clinical application, reproducibility, and understanding of the data used for model training. To overcome these limitations and improve patient and clinician trust, ML developers should provide open access to models, code, and source data, fostering iterative progress through community critique, thus enhancing model accuracy and mitigating biases.

LEVEL OF EVIDENCE

NA Laryngoscope, 2024.

Pleomorphic adenoma and carcinoma ex-pleomorphic adenoma tumorigenesis: A proteomic analysis

OBJECTIVES

To conduct a comprehensive proteomic analysis of normal salivary gland tissue, pleomorphic adenoma (PA), and carcinoma ex-pleomorphic adenoma (CXPA), and validate the proteomic findings using immunohistochemistry.

METHODS

Six normal salivary gland tissues, seven PA and seven CXPA samples underwent laser microdissection followed by liquid chromatography coupled to mass spectrometry. Protein identification and quantification were performed using MaxQuant software. Statistical analysis and functional enrichment were conducted using the Perseus platform and STRING tool, respectively. Immunohistochemistry was used for validation.

RESULTS

Comparative proteomic analysis revealed 2680 proteins across the three tissue types, with 799 significantly altered between groups. Translocation protein SEC63 homolog, Annexin A6 and Biglycan were up-regulated in CXPA compared to PA. Decorin was markedly up-regulated in both PA and CXPA compared to normal salivary gland (log2 fold changes of 7.58 and 7.38, respectively). Validation confirmed elevated levels of Biglycan and Decorin in the extracellular matrix of CXPA compared to PA.

CONCLUSIONS

Proteomic analysis identified differential protein expression patterns associated with malignant transformation of PA into CXPA. Findings indicate a crucial role for extracellular matrix proteins, specifically Biglycan and Decorin, in the tumorigenic progression of PA and CXPA.

Lactylome analyses suggest systematic lysine-lactylated substrates in oral squamous cell carcinoma under normoxia and hypoxia

Cancer cells tend to live in hypoxic environment characterized by enhanced glycolysis and accumulation of lactate. Intracellular lactate is shown to drive a novel type of post-translational modification (PTM), lysine lactylation (Kla). Kla has been confirmed to affect the malignant progression of tumors such as hepatocellular carcinoma (HCC) and colon cancer, whereas the global lactylomic profiling of oral squamous cell carcinoma (OSCC) is unclear. Here, the integrative lactylome and proteome analyses by using liquid chromatography-tandem mass spectrometry (LC-MS/MS) identified 1011 Kla sites within 532 proteins and 1197 Kla sites within 608 proteins in SCC25 cells under normoxic and hypoxic environments, respectively. Among these lactylated proteins, histones accounted for only a small fraction, suggesting the presence of Kla modification of OSCC in a large number of non-histone proteins. Notably, Kla preferred to enrich in spliceosome, ribosome and glycolysis/gluconeogenesis pathway in both normoxic and hypoxic cultures. Compared with normoxia, 589 differential proteins with 898 differentially lactylated sites were detected under hypoxia, which were mainly associated with the glycolysis/gluconeogenesis pathway by KEGG analysis. Importantly, we verified the presence of lactylation modification in the spliceosomal proteins hnRNPA1, SF3A1, hnRNPU and SLU7, as well as in glycolytic enzyme PFKP. In addition, the differential alternative splicing analysis described the divergence of pre-mRNA splicing patterns in the presence or absence of sodium lactate and at different oxygen concentrations. Finally, a negative correlation between tissue Kla levels and the prognosis of OSCC patients was revealed by immunohistochemistry. Our study is the first report to elucidate the lactylome and its biological function in OSCC, which deepens our understanding of the mechanisms underlying OSCC progression and provides a novel strategy for targeted therapy for OSCC.

Prospecting Specific Protein Patterns for High Body Mass Index (BMI), Metabolic Syndrome and Type 2 Diabetes in Saliva and Blood Plasma From a Brazilian Population

PURPOSE

Obesity and its associated metabolic disorders, such as T2DM and MeS, are a growing public health problem worldwide. Our goal was the identification of protein patterns that are uniquely characteristic of higher BMI, MeS, and T2DM in a Brazilian population.

EXPERIMENTAL DESIGN

Saliva and plasma proteomes, clinical parameters were analyzed in a population from the state of Rio de Janeiro, Brazil, a mixed-race population. Volunteers were sorted by their BMI into normal (n = 29), overweight (n = 25), and obese (n = 15) and were compared with individuals with MeS (n = 23) and T2DM (n = 11).

RESULTS

The Random Forest (RF) predictive model revealed that three clinical variables, BMI, HOMA-IR, and fasting blood glucose, are most important for predicting MeS and T2DM. A total of six plasmatic proteins (ABCD4, LDB1, PDZ, podoplanin, lipirin-alpha-3, and WRS) and six salivary proteins (hemoglobin subunit beta, POTEE, T cell receptor alpha variable 9-2, lactotransferrin, cystatin-S, carbonic anhydrase 6), are enhanced in T2DM and in MeS.

CONCLUSIONS AND CLINICAL RELEVANCE

Our data revealed similar alterations in protein composition across individuals with abnormal weight gain, T2DM, and MeS. This finding confirms the close link between these conditions at the molecular level in the studied population, potentially enhancing our understanding of these diseases and paving the way for the development of novel diagnostic tools.

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

Relationship between aggressive features of oral squamous cell carcinoma and the immunoexpression of CX3CR1, CX3CL1 and ITGAV

OBJECTIVE

We aimed to describe the association between CX3CR1, CX3CL1, and ITGAV immunoexpression with PNI and adverse oncologic outcomes in patients with OSCC.

STUDY DESIGN

Expression CX3CR1, CX3CL1, and ITGAV was assessed by immunohistochemistry in a cohort of 50 paraffin-embedded resections of OSCC. Survival analysis, Cox, and binary logistic regressions were undertaken to determine the impact on patient survival and predictive value for PNI.

RESULTS

CX3CL1 positive nerves exhibited a significant association with tumor budding (TB) (P = .043), whereas nerves positive for ITGAV were associated with PNI (P = .021), T3-T4 tumor size (P = .029), and III-IV stage (P = .044). Cases with ITGAV-positive nerves exhibited an odds ratio of 9.603 (P = .008) for PNI, whereas cases with CX3CL1-positive nerves exhibited and odds ratio of 4.682 (P = .033) for TB. A trend toward decreased 5-year overall survival (P = .078) and 5-year disease-specific survival (P = .09) was observed in relation to ITGAV-positive nerves. However, no independent predictors for poor survival were identified.

CONCLUSIONS

The expression of ITGAV was associated with PNI and advanced disease, whereas the expression of CX3CL1 was related to TB, suggesting that ITGAV and CX3CL1 are involved in their respective developments. Therefore, further investigations are encouraged to assess the potential utility of targeted therapies against CX3CL1 receptors in OSCC.

Mapping Conformational Changes in the Saliva Proteome Potentially Associated with Oral Cancer Aggressiveness

Diverse proteomics-based strategies have been applied to saliva to quantitatively identify diagnostic and prognostic targets for oral cancer. Considering that these targets may be regulated by events that do not imply variation in protein abundance levels, we hypothesized that changes in protein conformation can be associated with diagnosis and prognosis, revealing biological processes and novel targets of clinical relevance. For this, we employed limited proteolysis-mass spectrometry in saliva samples to explore structural alterations, comparing the proteome of healthy control and oral squamous cell carcinoma (OSCC) patients with and without lymph node metastasis. Thirty-six proteins with potential structural rearrangements were associated with clinical patient features including transketolase and its interacting partners. Moreover, N-glycosylated peptides contribute to structural rearrangements of potential diagnostic and prognostic markers. Altogether, this approach utilizes saliva proteins to search for targets for diagnosing and prognosing oral cancer and can guide the discovery of potential regulated sites beyond protein-level abundance.

Improving obesity research: Unveiling metabolic pathways through a 3D In vitro model of adipocytes using 3T3-L1 cells

Obesity, a burgeoning global health crisis, has tripled in prevalence over the past 45 years, necessitating innovative research methodologies. Adipocytes, which are responsible for energy storage, play a central role in obesity. However, most studies in this field rely on animal models or adipocyte monolayer cell cultures, which are limited in their ability to fully mimic the complex physiology of a living organism, or pose challenges in terms of cost, time consumption, and ethical considerations. These limitations prompt a shift towards alternative methodologies. In response, here we show a 3D in vitro model utilizing the 3T3-L1 cell line, aimed at faithfully replicating the metabolic intricacies of adipocytes in vivo. Using a workable cell line (3T3-L1), we produced adipocyte spheroids and differentiated them in presence and absence of TNF-α. Through a meticulous proteomic analysis, we compared the molecular profile of our adipose spheroids with that of adipose tissue from lean and obese C57BL/6J mice. This comparison demonstrated the model's efficacy in studying metabolic conditions, with TNF-α treated spheroids displaying a notable resemblance to obese white adipose tissue. Our findings underscore the model's simplicity, reproducibility, and cost-effectiveness, positioning it as a robust tool for authentically mimicking in vitro metabolic features of real adipose tissue. Notably, our model encapsulates key aspects of obesity, including insulin resistance and an obesity profile. This innovative approach has the potential to significantly impact the discovery of novel therapeutic interventions for metabolic syndrome and obesity. By providing a nuanced understanding of metabolic conditions, our 3D model stands as a transformative contribution to in vitro research, offering a pathway for the development of small molecules and biologics targeting these pervasive health issues in humans.

Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans

Introduction

This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients.

Methods

Computed tomography scans were collected from 78 patients with OSCC who underwent surgical treatment at a single medical center. We extracted 1,092 radiomic features from gross tumor volume in each patient's pre-treatment CT. Clinical characteristics were also obtained, including race, sex, age, tobacco and alcohol use, tumor staging, and treatment modality. A feature selection algorithm was used to eliminate the most redundant features, followed by a selection of the best subset of the Logistic regression model (LRM). The best LRM model was determined based on the best prediction accuracy in terms of the area under Receiver operating characteristic curve. Finally, significant radiomic features in the final LRM model were identified as imaging biomarkers.

Results and discussion

Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ=3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.

Shaping the future of oral cancer diagnosis: advances in salivary proteomics

INTRODUCTION

Saliva has gained increasing attention in the quest for disease biomarkers. Because it is a biological fluid that can be collected is an easy, painless, and safe way, it has been increasingly studied for the identification of oral cancer biomarkers. This is particularly important because oral cancer is often diagnosed at late stages with a poor prognosis.

AREAS COVERED

The review addresses the evolution of the experimental approaches used in salivary proteomics studies of oral cancer over the years and outlines advantages and pitfalls related to each one. In addition, examines the current landscape of oral cancer biomarker discovery and translation focusing on salivary proteomic studies. This discussion is based on an extensive literature search (PubMed, Scopus and Google Scholar).

EXPERT OPINION

The introduction of mass spectrometry has revolutionized the study of salivary proteomics. In the future, the focus will be on refining existing methods and introducing powerful experimental techniques such as mass spectrometry with selected reaction monitoring, which, despite their effectiveness, are still underutilized due to their high cost. In addition, conducting studies with larger cohorts and establishing standardized protocols for salivary proteomics are key challenges that need to be addressed in the coming years.

Proteome profiling of Paenibacillus sonchi genomovar Riograndensis SBR5T under conventional and alternative nitrogen fixation

Paenibacillus sonchi SBR5T is a Gram-positive, endospore-forming facultative aerobic diazotrophic bacterium that can fix nitrogen via an alternative Fe-only nitrogenase (AnfHDGK). In several bacteria, this alternative system is expressed under molybdenum (Mo)-limiting conditions when the conventional Mo-dependent nitrogenase (NifHDK) production is impaired. The regulatory mechanisms, metabolic processes, and cellular functions of N2 fixation by alternative and/or conventional systems are poorly understood in the Paenibacillus genus. We conducted a comparative proteomic profiling study of P. sonchi SBR5T grown under N2-fixing conditions with and without Mo supply through an LC-MS/MS and label-free quantification analysis to address this gap. Protein abundances revealed overrepresented processes related to anaerobiosis growth adaption, Fe-S cluster biosynthesis, ammonia assimilation, electron transfer, and sporulation under N2-fixing conditions compared to non-fixing control. Under Mo limitation, the Fe-only nitrogenase components were overrepresented together with the Mo-transporter system, while the dinitrogenase component (NifDK) of Mo‑nitrogenase was underrepresented. The dinitrogenase reductase component (NifH) and accessory proteins encoded by the nif operon had no significant differential expression, suggesting post-transcriptional regulation of nif gene products in this strain. Overall, this was the first comprehensive proteomic analysis of a diazotrophic strain from the Paenibacillaceae family, and it provided insights related to alternative N2-fixation by Fe-only nitrogenase. SIGNIFICANCE: In this work, we try to understand how the alternative nitrogen fixation system, presented by some diazotrophic bacteria, works. For this, we used the SBR5 lineage of P. sonchi, which presents the alternative system in which the nitrogenase cofactor is composed only of iron. In addition, we tried to unravel the proteome of this strain in different situations of nitrogen fixation, since, for Gram-positive bacteria, these systems are little known. The results achieved, through LC-MS/MS and label-free quantitative analysis, showed an overrepresentation of proteins related to different processes involved with growth under stressful conditions in situations of nitrogen deficiency, in addition to suggesting that some encoded proteins by the nif operon may be regulated at post-transcriptional levels. Our findings represent important steps toward the elucidation of nitrogen fixation systems in Gram-positive diazotrophic bacteria.

HormoNet: a deep learning approach for hormone-drug interaction prediction

Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is essential to understand the hormone-drug associations. Here, we present HormoNet to predict the HDI pairs and their risk level by integrating features derived from hormone and drug target proteins. To the best of our knowledge, this is one of the first attempts to employ deep learning approach for prediction of HDI prediction. Amino acid composition and pseudo amino acid composition were applied to represent target information using 30 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied synthetic minority over-sampling technique technique. Additionally, we constructed novel datasets for HDI prediction and the risk level of their interaction. HormoNet achieved high performance on our constructed hormone-drug benchmark datasets. The results provide insights into the understanding of the relationship between hormone and a drug, and indicate the potential benefit of reducing risk levels of interactions in designing more effective therapies for patients in drug treatments. Our benchmark datasets and the source codes for HormoNet are available in: https://github.com/EmamiNeda/HormoNet .

Radiomic Biomarkers of Locoregional Recurrence: Prognostic Insights from Oral Cavity Squamous Cell Carcinoma preoperative CT scans

This study aimed to identify CT-based imaging biomarkers for locoregional recurrence (LR) in Oral Cavity Squamous Cell Carcinoma (OSCC) patients. Our study involved a retrospective review of 78 patients with OSCC who underwent surgical treatment at a single medical center. An approach involving feature selection and statistical model diagnostics was utilized to identify biomarkers. Two radiomics biomarkers, Large Dependence Emphasis (LDE) of the Gray Level Dependence Matrix (GLDM) and Long Run Emphasis (LRE) of the Gray Level Run Length Matrix (GLRLM) of the 3D Laplacian of Gaussian (LoG σ = 3), have demonstrated the capability to preoperatively distinguish patients with and without LR, exhibiting exceptional testing specificity (1.00) and sensitivity (0.82). The group with LRE > 2.99 showed a 3-year recurrence-free survival rate of 0.81, in contrast to 0.49 for the group with LRE ≤ 2.99. Similarly, the group with LDE > 120 showed a rate of 0.82, compared to 0.49 for the group with LDE ≤ 120. These biomarkers broaden our understanding of using radiomics to predict OSCC progression, enabling personalized treatment plans to enhance patient survival.

Circ_0058063 Contributed to Oral Squamous Cell Carcinoma Development by Sponging miR-145 and Regulating PI3K/AKT Pathway

BACKGROUND

Circular RNAs (circRNAs) are key regulators of oral squamous cell carcinoma (OSCC) progression. In this study, we aimed to clarify the regulatory roles of circ_0058063 and its effect on tumorigenesis in OSCC.

METHODS

Quantitative real-time polymerase chain reaction was conducted to determine the expression levels of microRNA (miR)-145-5p and circ_0058063 in OSCC. Cell viability, adhesion, migration, and epithelial-mesenchymal transition (EMT) of OSCC cells were assessed using cell counting kit-8, cell adhesion, and transwell assays. Western blotting was performed to determine the phosphoinositide 3-kinase (PI3K) and protein kinase B (AKT) phosphorylation levels. Xenograft tumor models were constructed to evaluate the tumorigenicity of OSCC cells in vivo. In addition, the interaction between circ_0058063 and miR-145-5p was validated via luciferase reporter and RNA immunoprecipitation assays.

RESULTS

Expression levels of circ_0058063 were elevated, whereas those of miR-145-5p were decreased in OSCC. Upregulation of circ_0058063 levels enhanced the viability, adhesion, migration, and EMT of OSCC cells in vitro and promoted tumorigenicity in vivo. Moreover, circ_0058063 promoted OSCC growth by upregulating the PI3K and AKT phosphorylation levels. miR-145-5p overexpression considerably inhibited the PI3K/AKT pathway and decreased OSCC cell viability, adhesion, migration, and EMT. Mechanistically, circ_0058063 sponged miR-145-5p and activated the PI3K/AKT pathway in OSCC cells.

CONCLUSION

Our results revealed that circ_0058063 functions as an oncogene via regulation of the PI3K/AKT pathway by targeting miR-145-5p in OSCC, suggesting its potential for OSCC diagnosis and treatment.

Current standards in the diagnosis and treatment of oral squamous cell carcinoma - a multicenter analysis

The German guideline for oral squamous cell carcinoma (OSCC) describes the recommended diagnosis and treatment procedures for OSCC and ensures the highest quality patient care. However, the current German guideline for OSCC is indistinct and therapy planning is not standardized in detail between centers. To address this, the current diagnostic and therapeutic strategies in different oral and maxillofacial surgery departments in Germany were summarized using a uniform questionnaire. The results revealed high standards in oncologic maxillofacial care, but non-uniform standards exist between centers. Moreover, an increasing use of diagnostic and treatment methods that are not included in the German guideline for OSCC, such as positron emission tomography computed tomography (PET-CT) and tumor biomarkers, were used by different centers. These results support the updated German guideline for OSCC but highlight the need to consider other additive methods to improve patient care and outcomes. Furthermore, a recommendation to introduce tumor passports to simplify OSCC diagnosis and treatment should be discussed. These changes will improve the prognosis and quality of life of patients with OSCC.

Noninvasive radiomic analysis of enhanced CT predicts CTLA4 expression and prognosis in head and neck squamous cell carcinoma

Developing a radiomic model to predict CTLA4 expression levels and assessing its prognostic accuracy for patients. Medical imaging data were sourced from the TCIA database, while transcriptome sequencing data were derived from the TCGA database. We utilized a linear kernel SVM algorithm to develop a radiomic model for predicting CTLA4 gene expression. We then assessed the model's clinical relevance using survival and Cox regression analyses. Performance evaluations of the model were illustrated through ROC, PR, calibration, and decision curves. (1) Bioinformatics analysis: Kaplan-Meier curves indicated that increased CTLA4 expression correlates with enhanced overall survival (OS) (p < 0.001). Both univariate and multivariate analyses revealed that high CTLA4 expression served as a protective factor for OS (HR = 0.562, 95% CI 0.427-0.741, p < 0.001). (2) Radiomics evaluation: the ROC curve demonstrated that the AUC for the SVM radiomics model was 0.766 in the training set and 0.742 in the validation set. The calibration curve affirmed that the model's prediction probability for high gene expression aligns with the actual outcomes. Furthermore, decision curve analysis (DCA) indicated that our model boasts robust clinical applicability. CTLA4 expression level serves as an independent prognostic factor for HNSCCs. Using enhanced CT images, the SVM radiomic model effectively predicts CTLA4 expression levels. As a result, this model offers strong prognostic insights for HNSCCs, guiding precise diagnosis, treatment, and assisting in clinical decision-making.

Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives

Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.

Identification of Proteomic Biomarkers in Proliferative Verrucous Leukoplakia through Liquid Chromatography With Tandem Mass Spectrometry

Proliferative verrucous leukoplakia (PVL) is an oral potentially malignant disorder associated with high risk of malignant transformation. Currently, there is no treatment available, and restrictive follow-up of patients is crucial for a better prognosis. Oral leukoplakia (OL) shares some clinical and microscopic features with PVL but exhibits different clinical manifestations and a lower rate of malignant transformation. This study aimed to investigate the proteomic profile of PVL in tissue and saliva samples to identify potential diagnostic biomarkers with therapeutic implications. Tissue and saliva samples obtained from patients with PVL were compared with those from patients with oral OL and controls. Label-free liquid chromatography with tandem mass spectrometry was employed, followed by qualitative and quantitative analyses, to identify differentially expressed proteins. Potential biomarkers were identified and further validated using immunohistochemistry. Staining intensity scan analyses were performed on tissue samples from patients with PVL, patients with OL, and controls from Brazil, Spain, and Finland. The study revealed differences in the immune system, cell cycle, DNA regulation, apoptosis pathways, and the whole proteome of PVL samples. In addition, liquid chromatography with tandem mass spectrometry analyses showed that calreticulin (CALR), receptor of activated protein C kinase 1 (RACK1), and 14-3-3 Tau-protein (YWHAQ) were highly expressed in PVL samples. Immunohistochemistry validation confirmed increased CARL expression in PVL compared with OL. Conversely, RACK1 and YWHA were highly expressed in oral potentially malignant disorder compared to the control group. Furthermore, significant differences in CALR and RACK1 expression were observed in the OL group when comparing samples with and without oral epithelial dysplasia, unlike the PVL. This research provides insights into the molecular mechanisms underlying these conditions and highlights potential targets for future diagnostic and therapeutic approaches.

Dissecting the single-cell transcriptome network of macrophage and identifies a signature to predict prognosis in lung adenocarcinoma

PURPOSE

The tumor immune microenvironment (TME) plays a vital role in tumorigenesis, progression, and treatment. Macrophages, as an important component of the tumor microenvironment, play an essential role in antitumor immunity and TME remodeling. In this study, we aimed to explore the different functions of different origins macrophages in TME and their value as potential predictive markers of prognosis and treatment.

METHODS

We performed single-cell analysis using 21 lung adenocarcinoma (LUAD), 12 normal, and four peripheral blood samples from our data and public databases. A prognostic prediction model was then constructed using 502 TCGA patients and explored the potential factors affecting prognosis. The model was validated using data from 4 different GEO datasets with 544 patients after integration.

RESULTS

According to the source of macrophages, we classified macrophages into alveolar macrophages (AMs) and interstitial macrophages (IMs). AMs mainly infiltrated in normal lung tissue and expressed proliferative, antigen-presenting, scavenger receptors genes, while IMs occupied the majority in TME and expressed anti-inflammatory, lipid metabolism-related genes. Trajectory analysis revealed that AMs rely on self-renew, whereas IMs originated from monocytes in the blood. Cell-to-cell communication showed that AMs interacted mainly with T cells through the MHC I/II signaling pathway, while IMs mostly interacted with tumor-associated fibrocytes and tumor cells. We then constructed a risk model based on macrophage infiltration and showed an excellent predictive power. We further revealed the possible reasons for its potential prognosis prediction by differential genes, immune cell infiltration, and mutational differences.

CONCLUSION

In conclusion, we investigated the composition, expression differences, and phenotypic changes of macrophages from different origins in lung adenocarcinoma. In addition, we developed a prognostic prediction model based on different macrophage subtype infiltration, which can be used as a valid prognostic biomarker. New insights were provided into the role of macrophages in the prognosis and potential treatment of LUAD patients.

Synthesis of S-Carbamidomethyl Cysteine and Its Use for Quantification of Cysteinyl Peptides by Targeted Proteomics

Proteomics has played a central role in the identification of reliable disease biomarkers, which are the basis of precision medicine, a promising approach for tackling recalcitrant diseases such as cancer, that elude conventional treatments. Among proteomic methodologies, targeted proteomics employing stable isotope-labeled (SIL) internal standards is particularly suited for the clinical translation of biomarker information owing to its high throughput and accuracy in the quantitative analysis of patient-derived proteomes. Using SIL internal standards ensures the utmost level of confidence in detection and precision in targeted MS experiments. For successfully establishing assays based on targeted proteomics, it is crucial to secure broad coverage when selecting the SIL standard peptide panel. However, cysteinyl peptides have often been excluded because of cysteine's high chemical reactivity. To address this limitation, a new cysteine building block was developed by incorporating a sulfhydryl group configured with an S-carbamidomethyl group, which is commonly used in proteome sampling. This compound was found to be chemically stable and applicable to a variety of solid-phase peptide synthesis (SPPS) campaigns. Furthermore, a direct comparison of the synthesized SIL peptides and tryptic endogenous peptides demonstrated the potential utility of an SPPS flow based on the new cysteine building block for improving the success of targeted proteomic applications.

MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data

Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results.

Assessing the Role of Trypsin in Quantitative Plasma and Single-Cell Proteomics toward Clinical Application

Mass spectrometry-based bottom-up proteomics is rapidly evolving and routinely applied in large-scale biomedical studies. Proteases are a central component of every bottom-up proteomics experiment, digesting proteins into peptides. Trypsin has been the most widely applied protease in proteomics due to its characteristics. With ever-larger cohort sizes and possible future clinical application of mass spectrometry-based proteomics, the technical impact of trypsin becomes increasingly relevant. To assess possible biases introduced by trypsin digestion, we evaluated the impact of eight commercially available trypsins in a variety of bottom-up proteomics experiments and across a range of protease concentrations and storage times. To investigate the universal impact of these technical attributes, we included bulk HeLa cell lysate, human plasma, and single HEK293 cells, which were analyzed over a range of selected reaction monitoring (SRM), data-independent acquisition (DIA), and data-dependent acquisition (DDA) instrument methods on three LC-MS instruments. The quantification methods employed encompassed both label-free approaches and absolute quantification utilizing spike-in heavy-labeled recombinant protein fragment standards. Based on this extensive data set, we report variations between commercial trypsins, their source, and their concentration. Furthermore, we provide suggestions on the handling of trypsin in large-scale studies.

Comparative proteomics of saliva of healthy and gingivitis individuals from Rio de Janeiro

PURPOSE

In this work, we identified human and bacterial proteomes in the saliva from volunteers with gingivitis or healthy.

EXPERIMENTAL DESIGN

The reported population consisted of 18 volunteers (six with gingivitis and 12 healthy controls). Proteomics characterization was performed using a quantitative mass spectrometry method.

RESULTS

A total of 74 human and 116 bacterial proteins were identified in saliva. The major functional category that was modified in the human proteome was the immune response, followed by transport and protease inhibition. In the bacterial proteome, most of the proteins identified were from the Fusobacteria phylum, followed by Chlamydiae and Spirochaetes.

CONCLUSIONS AND CLINICAL RELEVANCE

We observed statistically relevant differences in the data between the groups. The 15 most important human proteins affecting the variation between case and control groups included cystatin S, alpha amylase, lactotransferrin, and negative elongation factor E. We found that bacterial proteins from Porphyromonas gingivalis and Fusobacterium nucleatum subsp. nucleatum related to the red and orange complexes were closely correlated with the occurrence of periodontal diseases.

Aberrant AHRR, ADAMTS2 and FAM184 DNA Methylation: Candidate Biomarkers in the Oral Rinse of Heavy Smokers

OBJECTIVE

To identify DNA methylation patterns of heavy smokers in oral rinse samples.

METHODS

Genome-wide DNA methylation data was imported from Gene Expression Omnibus GSE70977 using the GEOquery package. Two independent sets were analyzed: (a) 71 epigenomes of cancer-free subjects (heavy smokers n = 37 vs. non-smokers n = 31); for concordance assessment (b) 139 oral-cancer patients' epigenomes (heavy smokers n = 92 vs. non-smokers n = 47). Differential DNA methylation for CpG positions and at the regional level was determined using Limma and DMRcate Bioconductor packages. The linear model included sex, age, and alcohol consumption. The statistical threshold was set to p < 0.05. Functional gene prioritization analysis was performed for gene-targeted analysis.

RESULTS

In individuals without cancer and heavy smokers, the FAM184B gene was found with two CpG positions differentially hypermethylated (p = 0.012 after FDR adjustment), in a region of 48 bp with an absolute methylation difference >10% between groups (p = 1.76 × 10^-8). In the analysis corresponding to oral-cancer patients, we found AHRR differentially hypomethylated cancer patients, but also in subjects without oral cancer in the targeted analyses. Remarkably, ADAMTS2 was found differentially hypermethylated in heavy smokers without a diagnosis of cancer in two consecutive probes cg05575921 (p = 3.13 × 10^-7) and cg10208897 (p = 1.36 × 10^-5).

CONCLUSIONS

Differentially methylated AHRR, ADAMTS2, and FAM184B genes are biomarker candidates in oral rinse samples.

Comprehensive glycoprofiling of oral tumours associates N-glycosylation with lymph node metastasis and patient survival

While altered protein glycosylation is regarded a trait of oral squamous cell carcinoma (OSCC), the heterogeneous and dynamic glycoproteome of tumour tissues from OSCC patients remain unmapped. To this end, we here employ an integrated multi-omics approach comprising unbiased and quantitative glycomics and glycoproteomics applied to a cohort of resected primary tumour tissues from OSCC patients with (n = 19) and without (n = 12) lymph node metastasis. While all tumour tissues displayed relatively uniform N-glycome profiles suggesting overall stable global N-glycosylation during disease progression, altered expression of six sialylated N-glycans was found to correlate with lymph node metastasis. Notably, glycoproteomics and advanced statistical analyses uncovered altered site-specific N-glycosylation revealing previously unknown associations with several clinicopathological features. Importantly, the glycomics and glycoproteomics data unveiled that comparatively high abundance of two core-fucosylated and sialylated N-glycans (Glycan 40a and Glycan 46a) and one N-glycopeptide from fibronectin were associated with low patient survival, while a relatively low abundance of N-glycopeptides from both afamin and CD59 were also associated with poor survival. This study provides novel insight into the complex OSCC tissue N-glycoproteome forming an important resource to further explore the underpinning disease mechanisms and uncover new prognostic glyco-markers for OSCC.

High-throughput proteomics profiling-derived signature associated with chemotherapy response and survival for stage II/III colorectal cancer

Adjuvant chemotherapy (ACT) is usually used to reduce the risk of disease relapse and improve survival for stage II/III colorectal cancer (CRC). However, only a subset of patients could benefit from ACT. Thus, there is an urgent need to identify improved biomarkers to predict survival and stratify patients to refine the selection of ACT. We used high-throughput proteomics to analyze tumor and adjacent normal tissues of stage II/III CRC patients with /without relapse to identify potential markers for predicting prognosis and benefit from ACT. The machine learning approach was applied to identify relapse-specific markers. Then the artificial intelligence (AI)-assisted multiplex IHC was performed to validate the prognostic value of the relapse-specific markers and construct a proteomic-derived classifier for stage II/III CRC using 3 markers, including FHL3, GGA1, TGFBI. The proteomics profiling-derived signature for stage II/III CRC (PS) not only shows good accuracy to classify patients into high and low risk of relapse and mortality in all three cohorts, but also works independently of clinicopathologic features. ACT was associated with improved disease-free survival (DFS) and overall survival (OS) in stage II (pN0) patients with high PS and pN2 patients with high PS. This study demonstrated the clinical significance of proteomic features, which serve as a valuable source for potential biomarkers. The PS classifier provides prognostic value for identifying patients at high risk of relapse and mortality and optimizes individualized treatment strategy by detecting patients who may benefit from ACT for survival.

Multiperspective quantitative tumor-stroma ratio reveals histological areas associated with poor outcomes in oral squamous cell carcinoma

AIMS

Different regions of oral squamous cell carcinoma (OSCC) have particular histopathological characteristics, and the individual histological characteristics of the tumors are poorly understood. Therefore, calculating the proportion of tumor cells in different regions that allow assessment of the prognostic outcomes for OSCC patients would be of great clinical significance.

METHODS AND RESULTS

We established an open-source software-based analytic pipeline that defines the inner tumor and invasive tumor front (ITF) in pancytokeratin-stained whole slide images (WSIs) and quantifies the tumor-stroma ratio (TSR) within the two regions. We applied this method to 114 patients with OSCC and predicted patient prognosis by the TSR. The proportion of tumor area in the inner tumor was generally higher than that in the ITF (p < 0.0001). TSR was an independent prognostic factor for overall survival (OS) (p = 0.016), disease-free survival (DFS) (p = 0.026), and relapse-free survival (RFS) (p = 0.037) in inner tumor, and TSR was an independent prognostic factor for OS (p = 0.00052), DFS (p = 0.035), and metastasis-free survival (MFS) (p = 0.038) in the ITF. Tumor-low status was associated with poorer prognosis. There was a significant correlation between the TSR and perineural invasion (PNI) in the inner tumor (p = 0.009).

CONCLUSIONS

The histopathological characteristics of different regions of OSCC may be used to develop the potential prognostic markers. The TSR of the inner tumor is more targeted in predicting prognosis and accurately assesses the risk of PNI+.

A Paper-Based Fluorescent Sensor for Rapid Early Screening of Oral Squamous Cell Carcinoma

Various types of sensors play an irreplaceable role in the detection of biomarkers, but their high cost and complicated operation make it difficult to benefit ordinary people. Herein, we develop a low-cost, double-layered, paper-based fluorescent sensor (CP/HQ) structurally consisting of the upper reaction layer loaded with two oxidases (lactate oxidase and choline oxidase) and the bottom fluorescent layer that physically associates with the porphine-grafted composite fluorescent polymer colloids (PF-PDMTP/HQ). Based on the dramatic and rapid fluorescence decrease of porphine induced by the oxidation between saliva and oxidases and subsequent fluorescence resonance energy transfer from oxidized hydroquinone, the resultant fluorescent paper sensor enables us to achieve visual detection of OSCC, which was further recognized by smartphone scanning as the grayscale variation. It was found that the linear sensing range of grayscale value are 10-200 μM for lactic acid and 10-100 μM for choline, with LODs of 5.7 and 8.9 μM, respectively. More importantly, the sensor can achieve a powerful detection capability comparable to that of high-performance liquid chromatography (HPLC) in clinical settings with simple operation, demonstrating its great application potential. Our proposed sensor not only improves the accuracy of OSCC diagnosis but also provides a valuable attempt for the device modification of polymer-sensing systems and the development of non-invasive and easy-to-operate disease screening methods.

New Insights into the Impact of Human Papillomavirus on Oral Cancer in Young Patients: Proteomic Approach Reveals a Novel Role for S100A8

Human papillomavirus (HPV) infection has recently been linked to a subset of cancers affecting the oral cavity. However, the molecular mechanisms underlying HPV-driven oral squamous cell carcinoma (OSCC) onset and progression are poorly understood.

METHODS

We performed MS-based proteomics profiling based on HPV status in OSCC in young patients, following biological characterization and cell assays to explore the proteome functional landscape.

RESULTS

Thirty-nine proteins are differentially abundant between HPV (+) and HPV (-) OSCC. Among them, COPS3, DYHC1, and S100A8 are unfavorable for tumor recurrence and survival, in contrast to A2M and Serpine1, low levels of which show an association with better DFS. Remarkably, S100A8 is considered an independent prognostic factor for lower survival rates, and at high levels, it alters tumor-associated immune profiling, showing a lower proportion of M1 macrophages and dendritic cells. HPV (+) OSCC also displayed the pathogen-associated patterns receptor that, when activated, triggered the S100A8 and NFκB inflammatory responses.

CONCLUSION

HPV (+) OSCC has a peculiar microenvironment pattern distinctive from HPV (-), involving the expression of pathogen-associated pattern receptors, S100A8 overexpression, and NFκB activation and responses, which has important consequences in prognosis and may guide therapeutic decisions.

Extracellular Vesicles from Different Sources of Mesenchymal Stromal Cells Have Distinct Effects on Lung and Distal Organs in Experimental Sepsis

The effects of the administration of mesenchymal stromal cells (MSC) may vary according to the source. We hypothesized that MSC-derived extracellular vesicles (EVs) obtained from bone marrow (BM), adipose (AD), or lung (L) tissues may also lead to different effects in sepsis. We profiled the proteome from EVs as a first step toward understanding their mechanisms of action. Polymicrobial sepsis was induced in C57BL/6 mice by cecal ligation and puncture (SEPSIS) and SHAM (control) animals only underwent laparotomy. Twenty-four hours after surgery, animals in the SEPSIS group were randomized to receive saline or 3 × 10⁶ MSC-derived EVs from BM, AD, or L. The diffuse alveolar damage was decreased with EVs from all three sources. In kidneys, BM-, AD-, and L-EVs reduced edema and expression of interleukin-18. Kidney injury molecule-1 expression decreased only in BM- and L-EVs groups. In the liver, only BM-EVs reduced congestion and cell infiltration. The size and number of EVs from different sources were not different, but the proteome of the EVs differed. BM-EVs were enriched for anti-inflammatory proteins compared with AD-EVs and L-EVs. In conclusion, BM-EVs were associated with less organ damage compared with the other sources of EVs, which may be related to differences detected in their proteome.

Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.

Different biological effects of exposure to far-UVC (222 nm) and near-UVC (254 nm) irradiation

Ultraviolet C (UVC) light has long been used as a sterilizing agent, primarily through devices that emit at 254 nm. Depending on the dose and duration of exposure, UV 254 nm can cause erythema and photokeratitis and potentially cause skin cancer since it directly modifies nitrogenated nucleic acid bases. Filtered KrCl excimer lamps (emitting mainly at 222 nm) have emerged as safer germicidal tools and have even been proposed as devices to sterilize surgical wounds. All the studies that showed the safety of 222 nm analyzed cell number and viability, erythema generation, epidermal thickening, the formation of genetic lesions such as cyclobutane pyrimidine dimers (CPDs) and pyrimidine-(6-4)-pyrimidone photoproducts (6-4PPs) and cancer-inducing potential. Although nucleic acids can absorb and be modified by both UV 254 nm and UV 222 nm equally, compared to UV 254 nm, UV 222 nm is more intensely absorbed by proteins (especially aromatic side chains), causing photooxidation and cross-linking. Here, in addition to analyzing DNA lesion formation, for the first time, we evaluated changes in the proteome and cellular pathways, reactive oxygen species formation, and metalloproteinase (MMP) levels and activity in full-thickness in vitro reconstructed human skin (RHS) exposed to UV 222 nm. We also performed the longest (40 days) in vivo study of UV 222 nm exposure in the HRS/J mouse model at the occupational threshold limit value (TLV) for indirect exposure (25 mJ/cm²) and evaluated overall skin morphology, cellular pathological alterations, CPD and 6-4PP formation and MMP-9 activity. Our study showed that processes related to reactive oxygen species and inflammatory responses were more altered by UV 254 nm than by UV 222 nm. Our chronic in vivo exposure assay using the TLV confirmed that UV 222 nm causes minor damage to the skin. However, alterations in pathways related to skin regeneration raise concerns about direct exposure to UV 222 nm.

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients, yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, the reiterative GAN-based training process allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.

Discovery proteomics reveals potential protein signature associated with malignant phenotype acquisition in pleomorphic adenoma

OBJECTIVE

To analyze the proteomic profile of salivary pleomorphic adenoma (PA) and carcinoma ex pleomorphic adenoma (CXPA) samples and correlate them with the malignant transformation of the PA.

MATERIALS AND METHODS

Thirty samples (10 PA, 16 CXPA, and 4 residual PA) were microdissected and submitted to liquid chromatography-tandem mass spectrometry (LC-MS/MS). The proteomic data and protein identification were analyzed through LC-MS/MS spectra using the MaxQuant software.

RESULTS

The proteomic analysis identified and quantified a total of 240 proteins in which 135 were found in PA, residual PA, and CXPA. The shared proteins were divided into six subgroups, and the proteins that showed statistically significant differences (p > 0.05) and fold-change > or <2.5 in one subgroup to another subgroup were included. Seven proteins (Apolipoprotein A-I-APOA1, haptoglobin-HP, protein of the synaptonemal complex 1-SYCP1, anion transport protein of band 3-SLC4A1, subunit μ1 of AP-1 complex-AP1M1, beta subunit of hemoglobin-HBB, and dermcidin-DCD) were classified as potential protein signatures, being HP, AP1M1, and HBB with higher abundance for PA to residual PA, APOA1 with higher abundance for PA to CXPA, SLC4A1 with lower abundance in the PA to CXPA, SYCP1with lower abundance for residual PA to CXPA, and DCD with higher abundance in the CXPA with epithelial differentiation to myoepithelial differentiation.

CONCLUSIONS

In this work, we demonstrated the comparative proteomic profiling of PA, residual PA, and CXPA, and seven were proposed as protein signatures, some of which may be associated with the malignant phenotype acquisition.

High-Multiplex Aptamer-Based Serum Proteomics to Identify Candidate Serum Biomarkers of Oral Squamous Cell Carcinoma

Improved serological biomarkers are needed for the early detection, risk stratification and treatment surveillance of patients with oral squamous cell carcinoma (OSCC). We performed an exploratory study using advanced, highly specific, DNA-aptamer-based serum proteomics (SOMAscan, 1305-plex) to identify distinct proteomic changes in patients with OSCC pre- vs. post-resection and compared to healthy controls. A total of 63 significantly differentially expressed serum proteins (each p < 0.05) were found that could discriminate between OSCC and healthy controls with 100% accuracy. Furthermore, 121 proteins were detected that were significantly altered between pre- and post-resection sera, and 12 OSCC-associated proteins reversed to levels equivalent to healthy controls after resection. Of these, 6 were increased and 6 were decreased relative to healthy controls, highlighting the potential relevance of these proteins as OSCC tumor markers. Pathway analyses revealed potential pathophysiological mechanisms associated with OSCC. Hence, quantitative proteome analysis using SOMAscan technology is promising and may aid in the development of defined serum marker assays to predict tumor occurrence, progression and recurrence in OSCC, and to guide personalized therapies.

Quantitative protein biomarker panels: a path to improved clinical practice through proteomics

The utilisation of protein biomarker panels, rather than individual protein biomarkers, offers a more comprehensive representation of human physiology. It thus has the potential to improve diagnosis, prognosis and the differentiation of responders from nonresponders in the context of precision medicine. Although several proteomic techniques exist for measuring biomarker panels, the integration of proteomics into clinical practice has been limited. In this Commentary, we highlight the significance of quantitative protein biomarker panels in clinical medicine and outline the challenges that must be addressed in order to identify the most promising panels and implement them in clinical routines to realise their medical potential. Furthermore, we argue that the absolute quantification of protein panels through targeted mass spectrometric assays remains the most promising technology for translating proteomics into routine clinical applications due to its high flexibility, low sample costs, independence from affinity reagents and low entry barriers for its integration into existing laboratory workflows.

Effect of Processing Methods of Human Saliva on the Proteomic Profile and Protein-Mediated Biological Processes

The use of saliva as a protein source prior to microbiological and biological assays requires previous processing. However, the effect of these processing methods on the proteomic profile of saliva has not been tested. Stimulated human saliva was collected from eight healthy volunteers. Non-processed saliva was compared with 0.22 μm filtered, 0.45 μm filtered, and pasteurized saliva, by liquid chromatography-mass spectrometry. Data are available via ProteomeXchange with identifier PXD039248. The effect of processed saliva on microbial adhesion was tested using bacterial and fungus species and in biological cell behavior using HaCaT immortalized human keratinocytes. Two hundred and seventy-eight proteins were identified in non-processed saliva, of which 54 proteins (≈19%) were exclusive. Saliva processing reduced identified proteins to 222 (≈80%) for the 0.22 μm group, 219 (≈79%) for the 0.45 μm group, and 201 (≈72%) for the pasteurized saliva, compared to non-processed saliva. The proteomic profile showed similar molecular functions and biological processes. The different saliva processing methods did not alter microbial adhesion (ANOVA, p > 0.05). Interestingly, pasteurized saliva reduced keratinocyte cell viability. Saliva processing methods tested reduced the proteomic profile diversity of saliva but maintained similar molecular functions and biological processes, not interfering with microbial adhesion and cell viability, except for pasteurization, which reduced cell viability.

A meta-analysis reveals the protein profile associated with malignant transformation of oral leukoplakia

The search for biomarkers associated with oral leukoplakia malignant transformation is critical for early diagnosis and improved prognosis of oral cancer patients. This systematic review and meta-analysis aimed to assess protein-based markers potentially associated with malignant transformation of oral leukoplakia. Five database and the grey literature were searched. In total, 142 studies were included for qualitative synthesis, where 173 proteins were investigated due to their potential role in malignant progression from oral leukoplakia (OL) to oral squamous cell carcinoma (OSCC). The abundance of these proteins was analyzed in fixed tissues and/or biofluid samples, mainly by immunohistochemistry and ELISA, and 12 were shared by both samples. Enrichment analysis revealed that the differential abundant proteins are mostly involved with regulation of cell death, regulation of cell proliferation, and regulation of apoptotic process. Also, these proteins are mainly expressed in the extracellular region (55.5%), cell surface (24.8%), and vesicles (49.1%). The meta-analysis revealed that the proteins related to tumor progression, PD-L1, Mdm2, and Mucin-4 were significantly associated with greater abundance in OSCC patients, with an Odds Ratio (OR) of 0.12 (95% CI: 0.04-0.40), 0.44 (95% CI: 0.24-0.81), and 0.18 (95% CI: 0.04-0.86), respectively, with a moderate certainty of evidence. The results indicate a set of proteins that have been investigated across OSCC initiation and progression, and whose transcriptional expression is associated with clinical characteristics relevant to the prognosis and aggressiveness. Further verification and validation of this biomarkers set are strongly recommended for future clinical application.

Advances in Electrochemical Biosensors Based on Nanomaterials for Protein Biomarker Detection in Saliva

The focus on precise medicine enhances the need for timely diagnosis and frequent monitoring of chronic diseases. Moreover, the recent pandemic of severe acute respiratory syndrome coronavirus 2 poses a great demand for rapid detection and surveillance of viral infections. The detection of protein biomarkers and antigens in the saliva allows rapid identification of diseases or disease changes in scenarios where and when the test response at the point of care is mandated. While traditional methods of protein testing fail to provide the desired fast results, electrochemical biosensors based on nanomaterials hold perfect characteristics for the detection of biomarkers in point-of-care settings. The recent advances in electrochemical sensors for salivary protein detection are critically reviewed in this work, with emphasis on the role of nanomaterials to boost the biosensor analytical performance and increase the reliability of the test in human saliva samples. Furthermore, this work identifies the critical factors for further modernization of the nanomaterial-based electrochemical sensors, envisaging the development and implementation of next-generation sample-in-answer-out systems.

Salivary inflammatory biomarkers are predictive of mild cognitive impairment and Alzheimer's disease in a feasibility study

Alzheimer's disease (AD) is an insidious disease. Its distinctive pathology forms over a considerable length of time without symptoms. There is a need to detect this disease, before even subtle changes occur in cognition. Hallmark AD biomarkers, tau and amyloid-β, have shown promising results in CSF and blood. However, detecting early changes in these biomarkers and others will involve screening a wide group of healthy, asymptomatic individuals. Saliva is a feasible alternative. Sample collection is economical, non-invasive and saliva is an abundant source of proteins including tau and amyloid-β. This work sought to extend an earlier promising untargeted mass spectrometry study in saliva from individuals with mild cognitive impairment (MCI) or AD with age- and gender-matched cognitively normal from the South Australian Neurodegenerative Disease cohort. Five proteins, with key roles in inflammation, were chosen from this study and measured by ELISA from individuals with AD (n = 16), MCI (n = 15) and cognitively normal (n = 29). The concentrations of Cystatin-C, Interleukin-1 receptor antagonist, Stratifin, Matrix metalloproteinase 9 and Haptoglobin proteins had altered abundance in saliva from AD and MCI, consistent with the earlier study. Receiver operating characteristic analysis showed that combinations of these proteins demonstrated excellent diagnostic accuracy for distinguishing both MCI (area under curve = 0.97) and AD (area under curve = 0.97) from cognitively normal. These results provide evidence for saliva being a valuable source of biomarkers for early detection of cognitive impairment in individuals on the AD continuum and potentially other neurodegenerative diseases.

Connecting multiple microenvironment proteomes uncovers the biology in head and neck cancer

The poor prognosis of head and neck cancer (HNC) is associated with metastasis within the lymph nodes (LNs). Herein, the proteome of 140 multisite samples from a 59-HNC patient cohort, including primary and matched LN-negative or -positive tissues, saliva, and blood cells, reveals insights into the biology and potential metastasis biomarkers that may assist in clinical decision-making. Protein profiles are strictly associated with immune modulation across datasets, and this provides the basis for investigating immune markers associated with metastasis. The proteome of LN metastatic cells recapitulates the proteome of the primary tumor sites. Conversely, the LN microenvironment proteome highlights the candidate prognostic markers. By integrating prioritized peptide, protein, and transcript levels with machine learning models, we identify nodal metastasis signatures in blood and saliva. We present a proteomic characterization wiring multiple sites in HNC, thus providing a promising basis for understanding tumoral biology and identifying metastasis-associated signatures.

Advances, obstacles, and opportunities for machine learning in proteomics

The fields of proteomics and machine learning are both large disciplines, each producing well over 5,000 publications per year. However, studies combining both fields are still relatively rare, with only about 2% of recent proteomics papers including machine learning. This review, which focuses on the intersection of the fields, is intended to inspire proteomics researchers to develop skills and knowledge in the application of machine learning. A brief tutorial introduction to machine learning is provided, and research advances that rely on both fields, particularly as they relate to proteomics tools development and biomarker discovery, are highlighted. Key knowledge gaps and opportunities for scientific advancement are also enumerated.

Reiterative modeling of combined transcriptomic and proteomic features refines and improves the prediction of early recurrence in squamous cell carcinoma of head and neck

BACKGROUND

Patients with squamous cell carcinoma of the head and neck (SCCHN) have a high-risk of recurrence. We aimed to develop machine learning methods to identify transcriptomic and proteomic features that provide accurate classification models for predicting risk of early recurrence in SCCHN patients.

METHODS

Clinical, genomic, transcriptomic and proteomic features distinguishing recurrence risk were examined in SCCHN patients from The Cancer Genome Atlas (TCGA). Recurrence within one year after treatment was classified as high-risk and no recurrence as low-risk.

RESULTS

No significant differences in individual clinicopathological characteristics, mutation profiles or mRNA expression patterns were seen between the groups using conventional statistical analysis. Using the machine learning algorithm, extreme gradient boosting (XGBoost), ten proteins (RAD50, 4E-BP1, MYH11, MAP2K1, BECN1, NF2, RAB25, ERRFI1, KDR, SERPINE1) and five mRNAs (PLAUR, DKK1, AXIN2, ANG and VEGFA) made the greatest contribution to classification. These features were used to build improved models in XGBoost, achieving the best discrimination performance when combining transcriptomic and proteomic data, providing an accuracy of 0.939 and an Area Under the ROC Curve (AUC) of 0.951.

CONCLUSIONS

This study highlights machine learning to identify transcriptomic and proteomic factors that play important roles in predicting risk of recurrence in patients with SCCHN and to develop such models by iterative cycles to enhance their accuracy, thereby aiding the introduction of personalized treatment regimens.

A novel necroptosis-related LncRNA signature for prediction of prognosis and therapeutic responses of head and neck squamous cell carcinoma

Background: Long non-coding RNAs (lncRNAs) play an essential role in the occurrence and prognosis of tumors, and it has great potential as biomarkers of tumors. However, the roles of Necroptosis-related lncRNA (NRLs) in Head and neck squamous cell carcinoma (HNSCC) remain elusive. Methods: We comprehensively analyzed the gene expression and clinical information of 964 HNSCC in four cohorts. LASSO regression was utilized to construct a necroptosis-related lncRNA prognosis signature (NLPS). We used univariate and multivariate regression to assess the independent prognostic value of NLPS. Based on the optimal cut-off, patients were divided into high- and low-risk groups. In addition, the immune profile, multi-omics alteration, and pharmacological landscape of NLPS were further revealed. Results: A total of 21 NRLs associated with survival were identified by univariate regression in four cohorts. We constructed and validated a best prognostic model (NLPS). Compared to the low-risk group, patients in the high group demonstrated a more dismal prognosis. After adjusting for clinical features by multivariate analysis, NLPS still displayed independent prognostic value. Additionally, further analysis found that patients in the low-risk group showed more abundant immune cell infiltration and immunotherapy response. In contrast, patients in the high-risk group were more sensitive to multiple chemotherapeutic agents. Conclusion: As a promising tool, the establishment of NLPS provides guidance and assistance in the clinical management and personalized treatment of HNSCC.