Cancer Proteomics Research Articles

Determination of Biomarker Candidates with Proteomics Approach in Small Cell Lung Cancer: NCI-H209 Cell Line

The primary building blocks of a cell membrane, proteins, play crucial roles in communication between cells as well as interactions with the extracellular matrix. Additionally, they make for an excellent resource for illness identification due to their potential as biomarkers. In order to perform the study, HEL-299 (CCL-137™) and NCI-H209 lung cells were incubated at 37°C in a chamber that contained 5% CO2. Trypsinization was used to transfer the cells into eppendorf tubes. Proteomics analyses were carried out using an LC-QTOF equipment, and the corresponding procedures of denaturation, alkynylation, trypsinization, and purification were carried out by adding the required chemicals. The Searchquie and PeptideShacker software interfaces were used to assess the analysis findings. Proteins that differ across groups are displayed by classifying them based on their roles as cellular components, molecular activities, and biological processes. Proteomics data showed that the lung cancer cell line NCI-H209 lacked 14 proteins that were present in the healthy lung cell HEL-299. These are the proteins ANK3, PIK3R2, INPP5F, HSF1, VIM, NFAM1, SHROOM3, ETV4, RNF31, LMNA, BRD8, PRTN3, TERT, SMAD9. There were discovered to be 5 distinct proteins in the lung cancer group compared to healthy lung HEL-299 cells. These proteins are AHSG, NCOA6, VCP, DNAJC19, NCL. Given the heterogeneity of lung cancer, a thorough and in-depth investigation of lung cancer proteome profiling is necessary for effective target treatment. The examination of proteins as prospective lung cancer biomarker candidates shows that it will make up a viable source for clinical investigations. These proteins differ in the direction of our results.

Dichotomous intronic polyadenylation profiles reveal multifaceted gene functions in the pan-cancer transcriptome

Alternative cleavage and polyadenylation within introns (intronic APA) generate shorter mRNA isoforms; however, their physiological significance remains elusive. In this study, we developed a comprehensive workflow to analyze intronic APA profiles using the mammalian target of rapamycin (mTOR)-regulated transcriptome as a model system. Our investigation revealed two contrasting effects within the transcriptome in response to fluctuations in cellular mTOR activity: an increase in intronic APA for a subset of genes and a decrease for another subset of genes. The application of this workflow to RNA-seq data from The Cancer Genome Atlas demonstrated that this dichotomous intronic APA pattern is a consistent feature in transcriptomes across both normal tissues and various cancer types. Notably, our analyses of protein length changes resulting from intronic APA events revealed two distinct phenomena in proteome programming: a loss of functional domains due to significant changes in protein length or minimal alterations in C-terminal protein sequences within unstructured regions. Focusing on conserved intronic APA events across 10 different cancer types highlighted the prevalence of the latter cases in cancer transcriptomes, whereas the former cases were relatively enriched in normal tissue transcriptomes. These observations suggest potential, yet distinct, roles for intronic APA events during pathogenic processes and emphasize the abundance of protein isoforms with similar lengths in the cancer proteome. Furthermore, our investigation into the isoform-specific functions of JMJD6 intronic APA events supported the hypothesis that alterations in unstructured C-terminal protein regions lead to functional differences. Collectively, our findings underscore intronic APA events as a discrete molecular signature present in both normal tissues and cancer transcriptomes, highlighting the contribution of APA to the multifaceted functionality of the cancer proteome.

Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe)

We propose a new method for the simultaneous selection and estimation of multivariate sparse additive models with correlated errors. Our method called Covariance Assisted Multivariate Penalized Additive Regression (CoMPAdRe) simultaneously selects among null, linear, and smooth non-linear effects for each predictor while incorporating joint estimation of the sparse residual structure among responses, with the motivation that accounting for inter-response correlation structure can lead to improved accuracy in variable selection and estimation efficiency. CoMPAdRe is constructed in a computationally efficient way that allows the selection and estimation of linear and non-linear covariates to be conducted in parallel across responses. Compared to single-response approaches that marginally select linear and non-linear covariate effects, we demonstrate in simulation studies that the joint multivariate modeling leads to gains in both estimation efficiency and selection accuracy, of greater magnitude in settings where signal is moderate relative to the level of noise. We apply our approach to protein-mRNA expression levels from multiple breast cancer pathways obtained from The Cancer Proteome Atlas and characterize both mRNA-protein associations and protein-protein subnetworks for each pathway. We find non-linear mRNA-protein associations for the Core Reactive, EMT, PIK-AKT, and RTK pathways. Supplementary Materials are available online.

MoPepGen: Rapid and Comprehensive Identification of Non-canonical Peptides.

Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteomic diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively generates non-canonical peptides in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it enumerates previously unobservable noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient detection and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.

Prognostic Protein Biomarker Screening for Thyroid Carcinoma Based on Cancer Proteomics Profiles.

Thyroid carcinoma (THCA) ranks among the most prevalent cancers globally. Integrating advanced genomic and proteomic analyses to construct a protein-based prognostic model promises to identify effective biomarkers and explore new therapeutic avenues. In this study, proteomic data from The Cancer Proteomics Atlas (TCPA) and clinical data from The Cancer Genome Atlas (TCGA) were utilized. Using Kaplan-Meier, Cox regression, and LASSO penalized Cox analyses, we developed a prognostic risk model comprising 13 proteins (S100A4, PAI1, IGFBP2, RICTOR, B7-H3, COLLAGENVI, PAR, SNAIL, FAK, Connexin-43, Rheb, EVI1, and P90RSK_pT359S363). The protein prognostic model was validated as an independent predictor of survival time in THCA patients, based on risk curves, survival analysis, receiver operating characteristic curves and independent prognostic analysis. Additionally, we explored the immune cell infiltration and tumor mutational burden (TMB) related to these features. Notably, our study proved a novel approach for predicting treatment responses in THCA patients, including those undergoing chemotherapy and targeted therapy.

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Imputation of cancer proteomics data with a deep model that learns from many datasets.

Missing values are a major challenge in the analysis of mass spectrometry proteomics data. Missing values hinder reproducibility, decrease statistical power for identifying differentially expressed (DE) proteins and make it challenging to analyze low-abundance proteins. We present Lupine, a deep learning-based method for imputing, or estimating, missing values in tandem mass tag (TMT) proteomics data. Lupine is, to our knowledge, the first imputation method that is designed to learn jointly from many datasets, and we provide evidence that this approach leads to more accurate predictions. We validated Lupine by applying it to TMT data from >1,000 cancer patient samples spanning ten cancer types from the Clinical Proteomics Tumor Atlas Consortium (CPTAC). Lupine outperforms the state of the art for TMT imputation, identifies more DE proteins than other methods, corrects for TMT batch effects, and learns a meaningful representation of proteins and patient samples. Lupine is implemented as an open source Python package.

Comparative profiling of whole-cell and exosome samples reveals protein signatures that stratify breast cancer subtypes

Identifying novel breast cancer biomarkers will improve patient stratification, enhance therapeutic outcomes, and help develop non-invasive diagnostics. We compared the proteomic profiles of whole-cell and exosomal samples of representative breast cancer cell subtypes to evaluate the potential of extracellular vesicles as non-invasive disease biomarkers in liquid biopsies. Overall, differentially-expressed proteins in whole-cell and exosome samples (which included markers for invasion, metastasis, angiogenesis, and drug resistance) effectively discriminated subtypes; furthermore, our results confirmed that the proteomic profile of exosomes reflects breast cancer cell-of-origin, which underscores their potential as disease biomarkers. Our study will contribute to identifying biomarkers that support breast cancer patient stratification and developing novel therapeutic strategies. We include an open, interactive web tool to explore the data as a molecular resource that can explain the role of these protein signatures in breast cancer classification.Graphical (A) We quantified proteomic profiles of breast cancer cells (BCCs) and breast cancer-derived exosomes (BCDEs) samples from four breast cancer cell lines representative of common breast cancer subtypes – Her2-positive (Her2, MDA-MB-453), Luminal A (LA, MCF7), Luminal B (LB, ZR-75), and triple-negative (TN, MDA-MB-231). (B) We independently performed four comparisons for BCCs and BCDEs, comparing each cell line against the remaining three. (C) We identified differentially-expressed proteins (DEPs) for BCCs and BCDEs, defined protein signatures, and functionally analyzed resulting networks. Through pathway inference analysis (PIA), we identified Kyoto Encyclopedia of genes and genomes (KEGG) subpaths and biological Gene Ontology (GO) terms that displayed differential activation. (D) We validated our proteomic signature using the Cancer Genome Atlas (TCGA) and the Cancer Proteome Atlas (TCPA) databases, verified that differentially-activated pathways in BCDEs caused a corresponding response in receptor cells, and confirmed that the BCDE proteomic signature reflects their cell-of-origin and identifies candidate disease biomarkers in liquid biopsies

Proteomic Profile of Endometrial Cancer: A Scoping Review.

Proteomics can be a robust tool in protein identification and regulation, allowing the discovery of potential biomarkers. In clinical practice, the management of endometrial cancer can be challenging. Thus, identifying promising markers could be beneficial, helping both in diagnosis and prognostic stratification, even predicting the response to therapy. Therefore, this manuscript systematically reviews the existing evidence of the proteomic profile of human endometrial cancer. The literature search was conducted via Medline (through PubMed) and the Web of Science. The inclusion criteria were clinical, in vitro, and in vivo original studies reporting proteomic analysis using all types of samples to map the human endometrial cancer proteome. A total of 55 publications were included in this review. Most of the articles carried out a proteomic analysis on endometrial tissue, serum and plasma samples, which enabled the identification of several potential diagnostic and prognostic biomarkers. In addition, eight articles were analyzed regarding the identified proteins, where three studies showed a strong correlation, sharing forty-five proteins. This analysis also allowed the identification of the 10 most frequently reported proteins in these studies: EGFR, PGRMC1, CSE1L, MYDGF, STMN1, CASP3 ANXA2, YBX1, ANXA1, and MYH11. Proteomics-based approaches pointed out potential diagnostic and prognostic candidates for endometrial cancer. However, there is a lack of studies exploring novel therapeutic targets.

Impelling Times for Tissue Section Proteomics in Cancer

Impelling Times for Tissue Section Proteomics in Cancer

Advances in the Clinical Application of High-throughput Proteomics.

High-throughput proteomics has become an exciting field and a potential frontier of modern medicine since the early 2000s. While significant progress has been made in the technical aspects of the field, translating proteomics to clinical applications has been challenging. This review summarizes recent advances in clinical applications of high-throughput proteomics and discusses the associated challenges, advantages, and future directions. We focus on research progress and clinical applications of high-throughput proteomics in breast cancer, bladder cancer, laryngeal squamous cell carcinoma, gastric cancer, colorectal cancer, and coronavirus disease 2019. The future application of high-throughput proteomics will face challenges such as varying protein properties, limitations of statistical modeling, technical and logistical difficulties in data deposition, integration, and harmonization, as well as regulatory requirements for clinical validation and considerations. However, there are several noteworthy advantages of high-throughput proteomics, including the identification of novel global protein networks, the discovery of new proteins, and the synergistic incorporation with other omic data. We look forward to participating in and embracing future advances in high-throughput proteomics, such as proteomics-based single-cell biology and its clinical applications, individualized proteomics, pathology informatics, digital pathology, and deep learning models for high-throughput proteomics. Several new proteomic technologies are noteworthy, including data-independent acquisition mass spectrometry, nanopore-based proteomics, 4-D proteomics, and secondary ion mass spectrometry. In summary, we believe high-throughput proteomics will drastically shift the paradigm of translational research, clinical practice, and public health in the near future.

Delineating protein biomarkers for gastric cancers: A catalogue of mass spectrometry-based markers and assessment of their suitability for targeted proteomics applications

Gastric cancer (GC) is a global health concern. To facilitate improved management of GCs, protein biomarkers have been identified through mass spectrometry-based proteomics platforms. In order to exhibit clinical utility of such data, we congregated over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the mass spectrometry platforms, association of the protein with infectious agents, protein identifiers, sample size and clinical characters of samples used with details on validation. Development of targeted proteomics methods is the cornerstone for pursuing these markers into clinical utility. Therefore, we developed Protein Biomarker Matrix for Gastric Cancer (PBMGC), a simple catalogue of robustness of each protein. This analysis yielded the identification of robust tissue, serum, and urine diagnostic and prognostic protein biomarker panels which can be further tested for their clinical utility. We also ascertained proteotypic tryptic peptides of 5631 proteins suitable for developing multiple reaction monitoring (MRM) assays. Extensive characterization of these peptides was carried out to record peptide ions, mass/charge and enhanced specific peptide features. With the vision of catering to proteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). Users can browse and download the data and improve GCPDB by submitting recently published data. SignificanceMass spectrometry-based proteomics platforms have accumulated substantial data on proteins differentially regulated in gastric cancer (GC) clinical samples. The utility of such data in clinical applications is limited by search for suitable biomarker panels for assessment of GCs.We assembled over 6800 differentially regulated proteins in GCs from proteomics studies and recorded the corresponding details including mass spectrometry platforms, status on the association of the protein with infectious agents, protein identifiers from different databases, sample size and clinical characters of samples used in test and control conditions along with details on their validation.Towards the vision of utilizing these markers in clinical assays, Protein Biomarker Matrix for Gastric Cancer (PBMGC) was developed and clinically relevant multi-protein panels were identified. We also demonstrated identification and characterization of tryptic proteotypic tryptic peptides of 5631 proteins biomarkers of GCs which are suitable for development of MRM assays in a SCIEX QTRAP instrument.Aimed to caterproteomics researchers, the data generated through this analysis has been catalogued at Gastric Cancer Proteomics DataBase (GCPDB) (https://ciods.in/gcpdb/). The users can browse and download details on different markers and improve GCPDB by submitting recently published data.Such an analysis could lay a cornerstone for building more such resources or conduct such analysis in different clinical conditions to uptake and develop targeted proteomics as the method of choice for clinical applications.

Deciphering the SOX4/MAPK1 regulatory axis: a phosphoproteomic insight into IQGAP1 phosphorylation and pancreatic Cancer progression

ObjectiveThis study aims to elucidate the functional role of IQGAP1 phosphorylation modification mediated by the SOX4/MAPK1 regulatory axis in developing pancreatic cancer through phosphoproteomics analysis.MethodsProteomics and phosphoproteomics data of pancreatic cancer were obtained from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Differential analysis, kinase-substrate enrichment analysis (KSEA), and independent prognosis analysis were performed on these datasets. Subtype analysis of pancreatic cancer patients was conducted based on the expression of prognostic-related proteins, and the prognosis of different subtypes was evaluated through prognosis analysis. Differential analysis of proteins in different subtypes was performed to identify differential proteins in the high-risk subtype. Clinical correlation analysis was conducted based on the expression of prognostic-related proteins, pancreatic cancer typing results, and clinical characteristics in the pancreatic cancer proteomics dataset. Functional pathway enrichment analysis was performed using GSEA/GO/KEGG, and most module proteins correlated with pancreatic cancer were selected using WGCNA analysis. In cell experiments, pancreatic cancer cells were grouped, and the expression levels of SOX4, MAPK1, and the phosphorylation level of IQGAP1 were detected by RT-qPCR and Western blot experiments. The effect of SOX4 on MAPK1 promoter transcriptional activity was assessed using a dual-luciferase assay, and the enrichment of SOX4 on the MAPK1 promoter was examined using a ChIP assay. The proliferation, migration, and invasion functions of grouped pancreatic cancer cells were assessed using CCK-8, colony formation, and Transwell assays. In animal experiments, the impact of SOX4 on tumor growth and metastasis through the regulation of MAPK1-IQGAP1 phosphorylation modification was studied by constructing subcutaneous and orthotopic pancreatic cancer xenograft models, as well as a liver metastasis model in nude mice.ResultsPhosphoproteomics and proteomics data analysis revealed that the kinase MAPK1 may play an important role in pancreatic cancer progression by promoting IQGAP1 phosphorylation modification. Proteomics analysis classified pancreatic cancer patients into two subtypes, C1 and C2, where the high-risk C2 subtype was associated with poor prognosis, malignant tumor typing, and enriched tumor-related pathways. SOX4 may promote the occurrence of the high-risk C2 subtype of pancreatic cancer by regulating MAPK1-IQGAP1 phosphorylation modification. In vitro cell experiments confirmed that SOX4 promoted IQGAP1 phosphorylation modification by activating MAPK1 transcription while silencing SOX4 inhibited the proliferation, migration, and invasion of pancreatic cancer cells by reducing the phosphorylation level of MAPK1-IQGAP1. In vivo, animal experiments further confirmed that silencing SOX4 suppressed the growth and metastasis of pancreatic cancer by reducing the phosphorylation level of MAPK1-IQGAP1.ConclusionThe findings of this study suggest that SOX4 promotes the phosphorylation modification of IQGAP1 by activating MAPK1 transcription, thereby facilitating the growth and metastasis of pancreatic cancer.

The prognostic significance of collagen VI in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by poor prognosis and lack of effective diagnostic and prognostic biomarkers. A rich and activated desmoplastic tumor stroma is considered a hallmark of PDAC, and therefore stromal components could be a source of possible new prognostic biomarkers. Expression of collagen VI (COL6) has been found to associate with prognosis in many forms of cancer but has been less extensively studied in PDAC. Some previous studies indicate that COL6 expression is upregulated in PDAC, and it could have prognostic value. In this study the expression of COL6 was analyzed by immunohistochemistry in tissue microarrays (TMAs) containing tumor tissue samples from 164 (n = 164) PDAC patients who had undergone surgical resection. Scoring results were combined with clinical data, and the prognostic significance of COL6 was estimated with Kaplan-Meier survival estimates and multivariable Cox regression analysis. COL6 protein expression patterns were further investigated in The Cancer Proteome Atlas (TCPA) dataset (n = 103) and in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset (n = 138). COL6 mRNA expression in PDAC tissue was investigated using The Cancer Genome Atlas (TCGA) by forming a dataset containing gene expression data and corresponding clinical variables of 168 (n = 168) PDAC patients. In this large PDAC TMA cohort we could not find statistically significant (p < 0.05) differences in survival when comparing patients with high and low protein expression of any of the analyzed COL6 α-chains (α1(VI): HR 0.90, 95% CI 0.64-1.28; α2(VI): HR 1.28, 95% CI 0.86-1.89; α3(VI): HR 0.91, 95%CI 0.64-1.29). Similar results were obtained when assessing expression of COL6 in public data from TCPA, CPTAC and TCGA. In contrast with previous studies and some other cancer forms, we did not find any association of COL6 tissue expression and PDAC survival on protein or mRNA level.

PCAS: An Integrated Tool for Multi-Dimensional Cancer Research Utilizing Clinical Proteomic Tumor Analysis Consortium Data.

Proteomics offers a robust method for quantifying proteins and elucidating their roles in cellular functions, surpassing the insights provided by transcriptomics. The Clinical Proteomic Tumor Analysis Consortium database, enriched with comprehensive cancer proteomics data including phosphorylation and ubiquitination profiles, alongside transcriptomics data from the Genomic Data Commons, allow for integrative molecular studies of cancer. The ProteoCancer Analysis Suite (PCAS), our newly developed R package and Shinyapp, leverages these resources to facilitate in-depth analyses of proteomics, phosphoproteomics, and transcriptomics, enhancing our understanding of the tumor microenvironment through features like immune infiltration and drug sensitivity analysis. This tool aids in identifying critical signaling pathways and therapeutic targets, particularly through its detailed phosphoproteomic analysis. To demonstrate the functionality of the PCAS, we conducted an analysis of GAPDH across multiple cancer types, revealing a significant upregulation of protein levels, which is consistent with its important biological and clinical significance in tumors, as indicated in our prior research. Further experiments were used to validate the findings performed using the tool. In conclusion, the PCAS is a powerful and valuable tool for conducting comprehensive proteomic analyses, significantly enhancing our ability to uncover oncogenic mechanisms and identify potential therapeutic targets in cancer research.

Chemical Proteomics–Guided Discovery of Covalent Ligands for Cancer Proteins

Advances in genome sequencing and editing technologies have enriched our understanding of the biochemical pathways that drive tumorigenesis. Translating this knowledge into new medicines for cancer treatment, however, remains challenging, and many oncogenic proteins have proven recalcitrant to conventional approaches for chemical probe and drug discovery. Here, we discuss how innovations in chemical proteomics and covalent chemistry are being integrated to identify and advance first-in-class small molecules that target cancer-relevant proteins. Mechanistic studies have revealed that covalent compounds perturb protein functions in cancer cells in diverse ways that include the remodeling of protein–protein and protein–RNA complexes, as well as through alterations in posttranslational modification. We speculate on the attributes of chemical proteomics and covalent chemistry that have enabled targeting of previously inaccessible cancer-relevant pathways and consider technical challenges that remain to be addressed in order to fully realize the druggability of the cancer proteome.

Cysteine substitutants emerge in lung cancer proteomes during arginine restriction

In this issue of Molecular Cell, Yang etal.1 find that arginine-to-cysteine substitutants are enriched in a subsetof lung cancer proteomes, potentiated by arginine deprivation, and promote resistance to chemotherapy.

Dual-Aptamer Recognition of DNA Logic Gate Sensor-Based Specific Exosomal Proteins for Ovarian Cancer Diagnosis.

Clinical diagnosis of ovarian cancer lacks high accuracy due to the weak selection of specific biomarkers along with the circumstance biomarkers localization. Clustering analysis of proteins transported on exosomes enables a more precise screening of effective biomarkers. Herein, through bioinformatics analysis of ovarian cancer and exosome proteomes, two coexpressed proteins, EpCAM and CD24, specifically enriched, were identified, together with the development of an as-derived dual-aptamer targeted exosome-based strategy for ovarian cancer screening. In brief, a DNA ternary polymer with aptamers targeting EpCAM and CD24 was designed to present a logic gate reaction upon recognizing ovarian cancer exosomes, triggering a rolling circle amplification chemiluminescent signal. A dynamic detection range of 6 orders of magnitude was achieved by quantifying exosomes. Moreover, for clinical samples, this strategy could accurately differentiate exosomes from healthy persons, other cancer patients, and ovarian cancer patients, enabling promising in situ detection. By accurately selecting biomarkers and constructing a dual-targeted exosomal protein detection strategy, the limitation of insufficient specificity of traditional protein markers was circumvented. This work contributed to the development of exosome-based prognosis monitoring in ovarian cancer through the identification of disease-specific exosome protein markers.

Urine proteome profile of firefighters with exposure to emergency fire-induced smoke: A pilot study to identify potential carcinogenic effects

Firefighters are frequently exposed to a variety of chemicals formed from smoke, which pose a risk for numerous diseases, including cancer. Comparative urine proteome profiling could significantly improve our understanding of the early detection of potential cancer biomarkers. In this study, for the first time, we conducted a comparative protein profile analysis of 20 urine samples collected from ten real-life firefighters prior to and following emergency fire-induced smoke. Using a label-free quantitative proteomics platform, we identified and quantified 1325 unique protein groups, of which 45 proteins showed differential expressions in abundance in response to fire-smoke exposure (post) compared to the control (pre). Pathway analysis showed proteins associated with epithelium development (e.g., RHCG, HEG1, ADAMTSL2) and Alzheimer's disease (SORL1) were significantly increased in response to smoke exposure samples. A protein-protein-network study showed a possible link between these differentially abundant proteins and the known cancer gene (TP53). Moreover, a cross-comparison analysis revealed that seven proteins—ALDH1A1, APCS, POMC, COL2A1, RDX, DDAH2, and SDC4 overlapped with the previously published urine cancer proteome datasets, suggesting a potential cancer risk. Our findings demonstrated that the discovery proteomic platform is a promising analytical technique for identifying potential non-invasive biomarkers associated with fire-smoke exposure in firefighters that may be related to cancer.

EPPK1 as a Prognostic Biomarker in Type I Endometrial Cancer and Its Correlation with Immune Infiltration.

Approximately 20% of patients with type I endometrial cancer (EC) of the uterus experience recurrence and metastasis. However, existing data do not provide sufficient evidence for the utility of protein levels as prognostic biomarkers in type I EC. This study aims to determine whether epiplakin1 (EPPK1) and progesterone receptor (PR) play a role in the recurrence and metastasis of type I EC. Following the Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) for assessing the quality of biomarker research results, a retrospective analysis was conducted on clinical information and tissue samples of type I EC patients. Protein expression data and clinical data for type I EC were downloaded from The Cancer Proteome Atlas (TCPA) database. We utilized the Kaplan-Meier (K-M) method and Cox proportional hazards regression analyses to evaluate whether epiplakin1 (EPPK1), progesterone receptor (PR) and certain clinical parameters can serve as independent prognostic factors. The Immune Cell Abundance Identifier (ImmuCellAI) and Cancer Immunome Atlas (TCIA) were employed to predict responses to immunotherapy. Immunohistochemistry was carried out to assess the expression of EPPK1 in type I EC. Type I EC patients with high EPPK1 and low PR expression had higher International Federation of Gynecology and Obstetrics (FIGO) stage, recurrence, and metastasis rates. Furthermore, EPPK1 was identified as an independent prognostic factor, and low expression of EPPK1 was predominantly observed in the POLE ultramutated (POLEmut) group, indicating a favorable prognosis. Additionally, the high EPPK1 expression group had a lower Immune Prognostic Score (IPS), suggesting that the high-expression group may not benefit from immune checkpoint inhibitors. High expression of EPPK1 is an independent prognostic factor in type I EC patients with low PR expression. It can identify a subgroup of patients at high risk of recurrence. A more aggressive treatment approach is recommended for these patients.