Journal of Proteome Research

Targeted Mass Spectrometry of Longitudinal Patient Sera Reveals LTBP1 as a Potential Surveillance Biomarker for High-Grade Serous Ovarian Carcinoma

High-grade serous ovarian carcinoma (HGSC) is the most prevalent subtype of epithelial ovarian cancer. The combination of a high rate of recurrence and novel therapies in HGSC necessitates an accurate assessment of the disease. Currently, HGSC response to treatment and recurrence are monitored via immunoassay of serum levels of the glycoprotein CA125. CA125 levels predictably rise at HGSC recurrence; however, it is likely that the disease is progressing even before it is detectable through CA125. This may explain why treating solely based on CA125 increase has not been associated with improved outcomes. Thus, additional biomarkers that monitor HGSC progression and cancer recurrence are needed. For this purpose, we developed a scheduled parallel reaction monitoring mass spectrometry (PRM-MS) assay for the quantification of four previously identified HGSC-derived glycopeptides (from proteins FGL2, LGALS3BP, LTBP1, and TIMP1). We applied the assay to quantify their longitudinal expression profiles in 212 serum samples taken from 34 HGSC patients during disease progression. Analyses revealed that LTBP1 best-mirrored tumor load, dropping as a result of cancer treatment in 31 out of 34 patients and rising at HGSC recurrence in 28 patients. Additionally, LTBP1 rose earlier during remission than CA125 in 11 out of 25 platinum-sensitive patients with an average lead time of 116.4 days, making LTBP1 a promising candidate for monitoring of HGSC recurrence.

Proteomic Landscapes of 3D and 2D Models of High-Grade Serous Ovarian Carcinoma: Implications for Carboplatin Response

High-grade serous ovarian carcinoma (HGSOC) is the most common form of ovarian cancer, and finding new treatments remains an unmet need. While drug discovery is typically performed in two-dimensional (2D) monolayers, three-dimensional (3D) culture systems better mimic the in vivo conditions. However, a comprehensive comparison of 3D versus 2D ovarian cancer models is lacking. Here, we quantitatively compared the whole cell proteomic signatures of four ovarian cell lines─PEO1, PEO4, UWB1.289, and UWB1.289+BRCA1─with different status of BRCA genes grown in 2D and 3D. Using isobaric labeling proteomics, we quantified 6404 proteins and identified 371 significantly and commonly altered proteins between 2D and 3D. Proteins upregulated in 3D were enriched for transmembrane transport and NADH:ubiquinone oxidoreductase complex I, while energy metabolism and cell growth pathways also showed dimensionality-dependent changes. Notably, membrane-associated proteins were downregulated in spheroids, particularly EGFR in PEO1. Furthermore, the 3D culture modulated the response to carboplatin, with an increased expression of drug resistance-associated proteins, including NDUF family members in all spheroid models. These findings underscore how culture dimensionality influences both the molecular landscape and the chemotherapeutic response of HGSOC cells and highlights candidate targets for overcoming carboplatin resistance.

OCIAD2 Promotes Cancer Progression via Metabolic Reprogramming in Lung Adenocarcinoma

Proteome-Wide Multipoint Internal Calibration Curves for Evaluating Peptide-Level Linearity in Relative Quantitative Proteomics

Mass spectrometry (MS)-based proteomics is known for its high accuracy in quantifying peptides and proteins using various calibration strategies including internal and external calibration curves. While external multipoint calibration curves are created from serial dilutions, they often fail to account for sample-specific matrix effects. In contrast, internal calibration curves account for the sample matrix but face scalability and cost challenges for whole proteome analyses. In this manuscript, we present a novel TMT-based multipoint internal calibration curve strategy, which enables the generation of internal calibration curves for all peptides identified within a proteome in a single experiment to assess their linearity prior relative quantification. We applied this strategy to human ovarian cancer cells to evaluate the linear quantitative responses of all of the identified peptides and reveal the significant proteome changes associated with cisplatin treatment.

Elucidating the Molecular Mechanisms of Hederagenin-Regulated Mitophagy in Cervical Cancer SiHa Cells through an Integrative Approach Combining Proteomics and Advanced Network Association Algorithm

Hederagenin (Hed), a natural triterpenoid, exhibits antitumor potential in cervical cancer. The present study was designed to explore Hed's regulatory mechanisms on mitophagy in SiHa cervical cancer cells, employing tandem mass tag (TMT) proteomics and an advanced network association algorithm (NAA). Our findings revealed that Hed decreased SiHa cell viability, induced apoptosis, and altered mitochondrial membrane potential. Notably, Hed inhibited mitophagic flux under both normoxic and hypoxic conditions. Through TMT proteomics analysis and innovative NAA, we identified a close association between the HIF-1 signaling pathway and mitophagy. Network analysis further suggested that Hed acts on a target network centered on SRC, STAT3, AKT1, and HIF1A. Western blot analysis confirmed the expression and phosphorylation status of these targets in response to Hed. This study elucidates the molecular mechanisms underlying Hed's regulation of mitophagy in SiHa cells, offering novel insights and potential therapeutic targets for cervical cancer treatment.

Toward Proteomic-Based Prediction of Ex Vivo Platinum Sensitivity in Ovarian Cancer Ascitic Cellular Aggregates: A Pilot Study

The accumulation of malignant ascites in the peritoneal cavity is a hallmark of high-grade serous ovarian cancer (HGSC). This fluid contains three-dimensional multicellular aggregates known as spheroids, which contribute to chemoresistance and are an accessible source of tumor material for proteomic-based biomarker discovery studies. Although heterogeneous ascitic spheroids can be generated from primary cell suspensions for ex vivo applications, they suffer from long generation times and reduced biological relevance. Here, we compare their

A Robust Strategy for High-Throughput and Deep Proteomics by Combining Narrow-Window Data-Independent Acquisition and Isobaric Mass Tagging

Data-independent acquisition (DIA) mass spectrometry systematically fragments all precursor ions within predefined isolation windows of a predefined mass-to-charge (

Advancing Plasma Proteomics for the Discovery of Ovarian Cancer Biomarkers

The development of ultrasensitive proteomic methods for detecting potential protein tumor biomarkers remains a key challenge in modern biomedicine. We integrated data from classical reference proteomic methods─both panoramic (DDA-Shotgun LC-MS/MS) and targeted (MRM)─with a novel AFM-based enrichment approach coupled to mass spectrometry (AFM-MS), providing lower detection limits. This integrated strategy enabled the compilation of an expanded list of proteins associated with ovarian cancer progression. We identified a panel of previously unreported, ovarian cancer-specific candidate markers. A total of 371 proteins were found to be potentially involved in the pathological process, with 33% detected exclusively by the ultrasensitive AFM-MS method and 26% discovered through metabolomic associations. Notably, 6% of the identified proteins correspond to previously recognized ovarian cancer-specific markers, validating our multiplatform approach. Nine potential biomarkers are proposed for the first time, including ATRN, CPN1, APOF, TGM3, and CRNN. Immunoglobulin variable region peptides were reclassified as low-specificity background signals due to their high abundance and inflammation dependence. The identified biomarkers are present in blood at concentrations ranging from 10

Development and Validation of a Predictive Model for Resistance to Platinum-Based Chemotherapy in Patients with Ovarian Cancer through Proteomic Analysis

Platinum resistance in ovarian cancer poses a significant challenge, substantially impacting patient outcomes. Developing an accurate predictive model is crucial for improving clinical decision-making and guiding treatment strategies. Proteomic data from 217 high-grade serous ovarian cancer (HGSOC) biospecimens obtained from JHU, PNNL, and PTRC were used to construct a prediction model for identifying individuals who are resistant to platinum-based chemotherapy. A total of 6437 common proteins were detected across all data sets, with 26 proteins overlapping between the development cohorts JHU and PNNL. Using LASSO and logistic regression analysis, a six-protein model (P31323_PRKAR2B, Q13309_SKP2, Q14997_PSME4, Q6ZRP7_QSOX2, Q7LGA3_HS2ST1, and Q7Z2Z2_EFL1) was developed, which accurately predicted platinum resistance, with an AUC of 0.964 (95% CI, 0.929-0.999). Internal validation by resampling resulted in a C-index of 0.972 (95% CI 0.894-0.988). External validation performed on the PTRC cohort achieved an AUC of 0.855 (95% CI 0.748-0.963). Calibration curves showed good consistency, and DCA indicated superior clinical utility. The model also performed well in predicting PFS and OS at various time points. Based on these proteins, our predictive model can precisely predict platinum response and survival outcomes in HGSOC patients, which can assist clinicians in promptly identifying potentially platinum-resistant individuals.

Detection of Ovarian Cancer Using Samples Sourced from the Vaginal Microenvironment

Mass spectrometry (MS) offers high levels of specificity and sensitivity in clinical applications, and we have previously been able to demonstrate that matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS is capable of distinguishing two-component cell mixtures at low limits of detection. Ovarian cancer is notoriously difficult to detect due to the lack of diagnostic techniques available to the medical community. By sampling a local microenvironment, such as the vaginal canal and cervix, a MS based method is presented for monitoring disease progression from proximal samples to the diseased tissue. A murine xenograft model of high grade serous ovarian carcinoma (HGSOC) was used for this study, and vaginal lavages were obtained from mice on a weekly basis throughout disease progression and subjected to our MALDI-TOF MS workflow followed by statistical analyses. Proteins in the 4-20 kDa region of the mass spectrum yielded a fingerprint that we could consistently measure over time that correlated with disease progression. These fingerprints were found to be largely stable across all mice, with the protein fingerprint converging toward the end point of the study. MALDI-TOF MS serves as a unique analytical technique for measuring a sampled vaginal microenvironment in a specific and sensitive manner for the detection of HGSOC in a murine model.

Comparable Plasma Lipid Changes in Patients with High-Grade Cervical Intraepithelial Neoplasia and Patients with Cervical Cancer

Cervical cancer is the fourth most prevalent cancer among women worldwide and usually develops from cervical intraepithelial neoplasia (CIN). In the present study, we compared alterations in lipids associated with high-grade CIN and cervical cancer with those associated with a normal status and low-grade CIN by performing global lipid profiling on plasma (66 healthy controls and 55 patients with CIN1, 44 with CIN2/3, and 60 with cervical cancer) using ultraperformance liquid chromatography/quadrupole time-of-flight mass spectrometry. We identified 246 lipids and found 31 lipids with similar alterations in both high-grade CIN and cervical cancer. Among these 31 lipids, four lipid classes (phosphatidylcholine, phosphatidylethanolamine, diglyceride, and free fatty acids) were identified as the major lipid classes with significant differences in the patients with CIN2/3 and cervical cancer compared to the healthy controls and the patients with CIN1. Lipid metabolites belonging to the same classes were positively correlated with each other. High-grade CIN and cervical cancer induce comparable changes in lipid levels, which are closely related to the development of cervical tumors. These results suggest that lipid profiling is a useful method for monitoring progression to cervical cancer.

Systematic Survey of the Regulatory Networks of the Long Noncoding RNA BANCR in Cervical Cancer Cells

Long noncoding RNAs (lncRNAs) are increasingly recognized as functional regulators of human cancers. BRAF-activated noncoding RNA (BANCR), an oncogenic lncRNA, has a carcinogenic effect on many types of cancers. However, the clinical significance and molecular mechanisms of BANCR in cervical cancer are still unclear. Here, we generated BANCR knockout cell lines

Proteomic Studies That Predict Patients’ Responses to High-Grade Serous Ovarian Cancer Treatments: A Systematic Review

Multilayered Regulatory Dynamics of p53 Mutations and Platinum Resistance in Ovarian Cancer

TP53 mutation-driven gene expression programs define oncogenic phenotypes. While extensive studies have concentrated on the transcriptome and proteome, post-transcriptional processes, particularly translational variation, remain underexplored. This study presents a comprehensive analysis of the transcriptomics, translatiomics, and proteomics dynamics in the ovarian cancer cell line SKOV3, with a focus on the effects of p53 missense mutations (R175H, R273H, and Y220C) on gene dosage fluctuations. Despite clear transcriptional differences between wild-type and mutant p53, we find that extensive translational and post-translational buffering processes attenuate these discrepancies, yielding comparatively stable protein abundances. Moreover, we delineate that the relative contributions of transcription output, translation engagement, and protein stability collectively shape the final protein abundance in the context of p53 mutations. Clinical proteomic analysis of platinum-resistant ovarian cancer tissues reveals tumor-specific factors and acquired resistance pathways linked to p53 mutations. Our findings elucidate the multilayered regulatory landscape of p53 mutations and identify potential risk factors for platinum resistance associated with these mutations.

Proteomic Biomarkers and Diagnostic Tools in Ovarian Cancer: Understanding Their Clinical Value and Limitations

Ovarian cancer (OC) is one of the most lethal gynecological cancers worldwide, with vague symptoms, an insidious onset, and high recurrence rates. With limitations in screening tests, OC is often diagnosed in late stages, resulting in high mortality rates and poor prognosis. To improve survival and quality of life for OC patients, there is an urgent need for effective biomarkers that can aid in early detection, treatment monitoring, and prognosis. Despite technological advancements, clinical applications remain limited and existing OC biomarkers often lack high sensitivity and specificity. As proteins are direct executors of biological processes, they are key to understanding the molecular and cellular mechanisms underlying pathological changes. Proteome-based biomarkers hold promise for improving OC diagnosis and management. Here, we review established and emerging technologies for identifying proteome-based biomarkers that, alone or in combination, could enhance OC diagnostics with a focus on future improvements. Single and multiple proteome biomarkers, including glycoproteome and peptidome-based ones, are assessed with respect to their sensitivity, specificity, and clinical utility for ovarian cancer diagnosis. Key diagnostic techniques are critically reviewed, including mass spectrometry-based methods for biomarker discovery, immunoassay-based approaches for biomarker validation and current clinical applications, and emerging technologies such as molecular Raman spectroscopy, which shows promise for identifying spectral markers linked to biomarkers and future clinical use. In future, a multiplexed biomarker panel─utilizing either single or multimodal diagnostic platforms─would offer diverse applications in ovarian cancer diagnosis, further strengthening its translational potential in clinical practice.

Novel NMR-Based Approach to Reveal the ‘Metabolic Fingerprint’ of Cytotoxic Gold Drugs in Cancer Cells

A combination of pathway enrichment and metabolite clustering analysis is used to interpret untargeted

Proteomics for Biomarker Discovery in Gynecological Cancers: A Systematic Review

The present study aims to summarize the current biomarker landscape in gynecological cancers (GCs) and incorporate bioinformatics analysis to highlight specific biological processes. The literature was retrieved from PubMed, Web of Science, Embase, Scopus, Ovid Medline, and Cochrane Library. The final search was conducted on December 7, 2022. Prospective registration was completed with the PROSPERO with registration number CRD42023477145. This systematic review covered proteomic research on biomarkers for cervical, endometrial, and ovarian cancers. The PANTHER classification system was used to classify the shortlisted candidate biomarkers (CBs), and the STRING database was utilized to visualize protein-protein interaction networks. A total of 23 articles were included in this systematic review. Consistently regulated CBs in the GCs include collagen alpha-2(I) chain, collagen alpha-1(III) chain, collagen alpha-2(V) chain, calreticulin, protein disulfide-isomerase A3, heat shock protein family A (Hsp70) member 5, prolyl 4-hydroxylase, beta polypeptide, fibrinogen alpha chain, fibrinogen gamma chain, apolipoprotein B-100, apolipoprotein C-IV, and apolipoprotein M. In conclusion, collagens, fibrinogens, chaperones, and apolipoproteins were revealed to be replicated in GCs and to be regulated consistently. These CBs contribute to GC etiology and physiology by participating in collagen fibril organization, blood coagulation, protein folding in endoplasmic reticulum, and lipid transporter activity.

Tissue-, Blood-, and Urine-Based Proteomics Changes of Different Endometrial Receptivity Status in Patients after Fertility-Sparing Treatment of Atypical Endometrial Hyperplasia or Endometrial Cancer

Liquid biopsy noninvasively characterizes diseases by analyzing biomarker proteins in biofluids, which provide valuable insights into physiological and pathological processes. In this study, we conducted an analysis of the differences between atypical endometrial hyperplasia or endometrial cancer (AH/EC) patients after fertility-sparing treatment with different RNA-Seq-based endometrial receptivity test (rsERT) results ("receptive" versus "pre-receptive"), to investigate the proteomic connections among tissue, serum, and urine samples. Samples of endometrial tissue, serum, and urine from 40 rsERT "pre-receptive" and 10 rsERT "receptive" patients were analyzed for proteomic profiling. We integrated differentially expressed proteins from three sample types to investigate endometrial receptor (ER)-related molecular changes. Our findings indicated that both serum and urine proteomes can serve as indicators of functional changes in endometrial tissue. In serum, proteins associated with cholesterol metabolism, immune response, and coagulation exhibited a differential expression. In urine, proteins related to immune function and metabolic processes demonstrated varying levels of expression. The differentially expressed proteins in both serum and urine were associated with the immune response and metabolism. In conclusion, biofluids serve as a reflection of functional changes in endometrial tissue, which will facilitate a deeper understanding of endometrial receptivity and the discovery of potential clinical biomarkers.

Deficiency of the UBE2C/F264 Axis Underlies Intrinsic Radioresistance in Cervical Carcinoma

Intrinsic radiotherapy resistance remains a persistent challenge to the treatment of cervical cancer (CC), as the underlying mechanisms remain largely elusive. In this study, proteomic profiling was combined with

SWATH-MS Analysis of FFPE Tissues Identifies Stathmin as a Potential Marker of Endometrial Cancer in Patients Exposed to Tamoxifen

A specific form of endometrial cancer (EC) can develop in breast cancer patients previously treated with tamoxifen (ET), an antagonist of estrogen receptor (ER) that inhibits proliferation of ER positive breast cancer. ET tumors have a different phenotype than endometrial tumors, which typically develop

Integrated Proteomics and Metabolomics Profiling Unveils Biomarkers and Immune Characteristics for Pelvic Lymph Node Metastasis in Cervical Cancer

Pelvic lymph node metastasis (PLNM) significantly affects the prognosis of cervical cancer (CC). However, current imaging examinations and serum squamous cell carcinoma antigen (SCCA) testing are inadequate for assessing the pelvic lymph node status in CC. To identify accurate noninvasive biomarkers for diagnosing PLNM and minimizing unnecessary postoperative lymphadenectomy and its associated complications, we performed a comprehensive proteomic and metabolomic analysis of plasma from 124 patients with CC, along with a proteomic analysis of 60 paired tissue samples. Through machine learning methods, we identified potential plasma biomarkers (TTR, MASP2, APOD, and 7α-hydroxy-cholestene-3-one) and constructed a diagnostic model. In the validation cohort, the diagnostic model combined with SCCA exhibited a higher sensitivity (72.4%) than SCCA (64.3%) and imaging examination (14.3%). The plasma protein biomarkers were consistently validated in paired tissue samples. Additionally, immune infiltration analysis demonstrated that CD4 and CD8 T cells were highly infiltrated in the PLNM group, suggesting a potentially enhanced response to immunotherapy. Here, we established a biomarker panel for PLNM and highlighted the altered immune characteristics associated with PLNM, offering valuable insights for the development of immunotherapy strategies for patients with PLNM.

Proteomics-Based Mapping of the Lymph Node Metastasis Landscape in Cervical Cancer

Lymph node metastasis (LNM) in cervical cancer is a critical determinant of the disease progression and prognosis. Elucidating its molecular mechanisms and identifying specific biomarkers are crucial for optimizing treatment. DIA-based quantitative proteomic sequencing was conducted on 49 cervical cancer patients. We screened differentially expressed proteins and performed functional enrichment, pathway scoring, time-series analysis, protein interaction studies, and integrated proteomic and TCGA transcriptomic data to identify biomarkers. 56 genes showed consistent expression trends, with 2 upregulated (SERPINB5, FABP5) and 54 downregulated (including ZNF512). Novel findings include activation of unsaturated fatty acid/steroid biosynthesis and inhibition of cell junction pathways during progression. Lymphovascular space invasion (LVSI) precedes LNM, but a subset of LNM cases lacks LVSI and exhibits a unique cholesterol metabolism activation. SERPINB5, FABP5, and ZNF512 were validated as potential prognostic and LNM-predictive biomarkers. DIA proteomics characterized cervical cancer lymph node metastasis, revealing molecular changes and potential biomarkers and offering new insights into its biological mechanisms.

The Multiomics Landscape of Plasma-Derived Small Extracellular Vesicles during the Development of HPV+ Cervical Cancer

The transition from HPV infection to cervical cancer (CC) remains unclear, especially concerning small extracellular vesicles (sEVs). We analyzed plasma-sEVs from HPV-negative individuals (HPVN-sEVs), HPV-positive individuals (HPVP-sEVs), and HPV-positive CC patients (HPVC-sEVs) via proteomics (n = 5/5/9) and transcriptomics (n = 3/3/4). Multiomics revealed HPVP-sEVs harbor antiviral and pro-inflammatory signals early in infection, whereas HPVC-sEVs shift toward immunosuppression to sustain persistent infection. Specifically, IFITM2─an antiviral gene─was markedly enriched in HPVP-sEVs versus HPVN-sEVs (

Investigating Cisplatin Resistance in Squamous Cervical Cancer: Proteomic Insights into DNA Repair Pathways and Omics-Based Drug Repurposing

Cisplatin-based chemotherapy is a cornerstone in treating cervical cancer, yet the efficacy is frequently limited by the rapid onset of drug resistance, a major challenge in clinical management. To investigate this, we employed HPV16+ human cervix squamous carcinoma cells, SiHa (CIS/S), and their cisplatin-resistant subline (CIS/R) as a model. Using DIA-based proteomics, we identified 5152 protein groups and over 50,000 peptides with a global FDR <1%. Comparative analysis revealed 123 differentially expressed proteins. Gene Set Enrichment Analysis (GSEA) highlighted proteins involved in DNA damage, metabolism, and repair pathways (RFC4, RFC3, RFC2, DUT, DDX54, CDCA8, CDK7, CHAF1B, and GTF2F1), suggesting a role in developing acquired cisplatin resistance. Pathways related to mitotic spindle assembly and P53 signaling were found to be perturbed in resistant cells. Next, we screened a library of approx. 240 FDA-approved drugs against three protein targets and found four small-molecular ligands as potential hits for further

Quantitative Proteomics Reveal the Mechanism of MiR-138–5p Suppressing Cervical Cancer via Targeting ZNF385A

MicroRNAs are short, noncoding RNA molecules that exert pivotal roles in cancer development and progression by modulating various target genes. There is growing evidence that miR-138-5p is significantly involved in cervical cancer (CC). However, its precise molecular mechanism has yet to be fully understood. In the current investigation, a quantitative proteomics approach was utilized to detect possible miR-138-5p targets in HeLa cells systematically. In total, 364 proteins were downregulated, and 150 were upregulated after miR-138-5p overexpression. Bioinformatic analysis of these differentially expressed proteins (DEPs) revealed significant enrichment in several cancer-related pathways. Zinc finger protein 385A (ZNF385A) was determined as a novel direct target of miR-138-5p and discovered to facilitate the proliferation, migration, and cell cycle progression of HeLa cells. SFN and Fas cell surface death receptor(FAS) were then identified as functional downstream effectors of ZNF385A and miR-138-5p. Moreover, a tumor xenograft experiment was conducted to validate the association of miR-138-5p-ZNF385A-SFN/FAS axis with the development of CC

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages featured more diverse lipid alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations underscored unique perturbations in cell membrane stability, proliferation, and survival during cancer development and progression, offering potential targets for early detection and prognosis of human ovarian cancer.

Impact of Cold Ischemia on the Stability of 1H-MRS-Detected Metabolic Profiles of Ovarian Cancer Specimens

Proton magnetic resonance spectroscopy (

High-Mannose N -Glycans To Monitor Early Response to Chemotherapy in African Epithelial Ovarian Cancer Patients─A Pilot Study