Journal of Proteomics

Proteomic analysis reveals CAAP1 negatively correlates with platinum resistance in ovarian cancer

The present study sought to investigate the correlation between CAAP1 and platinum resistance in ovarian cancer and to preliminarily explore the potential biological function of CAAP1. Proteomic analysis was used to analyze differentially expressed proteins in platinum-sensitive and -resistant tissue samples of ovarian cancer. The Kaplan-Meier plotter was used for prognostic analysis. Immunohistochemistry assay and chi-square test were employed to explore the relationship between CAAP1 and platinum resistance in tissue samples. Lentivirus transfection, immunoprecipitation-mass spectrometry, and bioinformatics analysis were used to determine the potential biological function of CAAP1. Based on results, the expression level of CAAP1 was significantly higher in platinum-sensitive tissues compared to that in resistant tissues. Chi-square test demonstrated that there is a negative correlation between high expression of CAAP1 and platinum resistance. Overexpression of CAAP1 increased cis‑platinum sensitivity of the A2780/DDP cell line likely via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. In summary, there is a negative correlation between high expression of CAAP1 and platinum resistance. CAAP1 might be a potential biomarker for platinum resistance in ovarian cancer. SIGNIFICANCE: Platinum resistance is a key factor affecting the survival of ovarian cancer patients. Understanding the mechanisms of platinum resistance is highly important for ovarian cancer management. Here, we performed the DIA- and DDA-based proteomics to analyze differentially expressed proteins in tissue and cell samples of ovarian cancer. We found that the protein identified as CAAP1, which was first reported to be involved in the regulation of apoptosis, may be negatively correlates with platinum resistance in ovarian cancer. In addition, we also found that CAAP1 enhanced the sensitivity of platinum-resistant cells to cis‑platinum via the mRNA splicing pathway by interacting with the splicing factor AKAP17A. Our data would be useful to reveal novel molecular mechanisms of platinum resistance in ovarian cancer.

Identification and validation of differential plasma proteins levels in epithelial ovarian cancer

Diagnosis of Ovarian cancer (OC) has been a challenge, the purpose, therefore is to identify plasma proteins differentially expressed in epithelial ovarian cancer patients. Human plasma samples from patients with OC (n = 138), benign tumors (n = 20) and controls (n = 238) were used. Tandem Mass Tag (TMT) based quantitative analysis by high resolution mass spectrometry, was followed by validation using Quantibody array and ELISA techniques. 507 plasma proteins showed differential protein levels in OC plasma samples. 21 proteins were validated using Quantibody array. Further, nine proteins (CA125, CFD, CST3, ICAM1, IGFBP2, IGFBP3, SPP1, TSP1 and VEGFA) which showed significant differences in protein levels in Quantibody array analysis were validated using ELISA. In ELISA, the levels of CA125, IGFBP2, ICAM1 and SPP1 were significantly increased and levels of Adipsin and TSP1 were decreased in tumors compared to controls and benign group. Epithelial ovarian cancer diagnosis model combining five markers (CA125, IGFBP2, SPP1, TSP1 and ADI) showed 90.24% sensitivity and 94.87% specificity. In conclusion a panel of 5 plasma proteins has been found to be useful in distinguishing plasma samples from epithelial ovarian cancers from patients with benign tumors and healthy normal subjects. This has the potential as a diagnostic assay for epithelial ovarian cancer. SIGNIFICANCE: The significance of this case-control study is based on the large and well defined ovarian cancer patient population (epithelial ovarian cancers including serous and mucinous subtypes), age matched controls and benign ovarian tumors. This study incorporates a discovery phase involving quantitative proteomic analysis of immune-depleted plasma followed by two levels of validation studies involving a selected list of proteins using antibody arrays and ELISA. The validations were performed on an independent set of samples comprising of epithelial ovarian cancer subtypes, controls and benign tumors. The multiple marker combination comprising of Adipsin, CA125, IGFBP2, SPP1 and TSP1 identified in the study by ELISA could enable rapid translation to a larger screening study.

Use of the serum glycan state to predict ovarian cancer patients' clinical response to chemotherapy treatment

Ovarian cancer is the most lethal gynecologic carcinoma; because the tumor often relapses shortly after treatment. Glycosylation plays important roles in cancer drug resistance and could be used as biomarkers to predict the drug response of patients. We used MALDI-QIT-TOF MS to analyze the serum glycomic from patients with different drug responses. Samples were collected before treatment; follow-up visit were performed after 6 months. Forty-eight drug-sensitive patients and 16 drug-resistant patients were enrolled. Compared with drug-sensitive patients, 5 glyco-subclasses and 5 single glycans were significantly altered in drug-resistant patients. Lewis type, α2,3 sialic acid and multibranch glycans were increased, α2,6 sialic acid glycans were decreased. The peak at m/z 2986.44 showed stronger prediction abilities than other single glycans, with an AUC of 0.83. A panel of three increased glycans (m/z 2401.36, H5N4F1S2, a Lewis type biantennary glycan; m/z 2986.44, H6N5S3, a triantennary trisialylated glycan; m/z 3086.39, H6N5F1S3, a Lewis type triantennary glycan) combined with CA125 achieved an AUC value of 0.88, showing a strong discrimination performance. This study provides new insights into N-glycosylation patterns in ovarian cancer patients with different drug response. These altered glycans might serve as biomarkers to reflect patients' drug sensitivity and to guide clinical treatment. SIGNIFICANCE: A large number of ovarian cancer patients experience tumor relapse shortly after initial treatment. Glycosylation plays important roles in cancer drug resistance and could be used as a biomarker to predict the drug response of patients. However, the glycosylation expressed in patients with different drug response have not been elucidated. In the present study, we used MALDI-QIT-TOF MS to analyze the serum glycomic levels of patients with different drug responses. Several glycans were changed significantly between these two groups. A panel of three increased glycans (m/z 2401.36, a Lewis type biantennary glycan, 2986.44, a triantennary trisialylated glycan, and 3086.39, a Lewis type triantennary glycan) combined with CA125 performed better descrimination of these two groups with AUC of 0.88. These altered glycans might serve as biomarkers to reflect patients' drug sensitivity and to guide clinical treatment.

Candidate prognostic biomarkers and prediction models for high-grade serous ovarian cancer from urinary proteomics

High-grade serous ovarian cancer (HGSOC) is one of the most common histologic types of ovarian cancer. The purpose of this study was to identify potential prognostic biomarkers in urine specimens from patients with HGSOC. First, 56 urine samples with information on relapse-free survival (RFS) months were collected and classified into good prognosis (RFS ≥ 12 months) and poor prognosis (RFS < 12 months) groups. Next, data-independent acquisition (DIA)-based mass spectrometry (MS) analysis was combined with MSFragger-DIA workflow to identify potential prognostic biomarkers in a discovery set (n = 31). With the aid of parallel reaction monitoring (PRM) analysis, four candidate biomarkers (ANXA1, G6PI, SPB3, and SPRR3) were finally validated in both the discovery set and an independent validation set (n = 25). Subsequent RFS and Cox regression analyses confirmed the utility of these candidate biomarkers as independent prognostic factors affecting RFS in patients with HGSOC. Regression models were constructed to predict the 12-month RFS rate, with area under the receiver operating characteristic curve (AUC) values ranging from 0.847 to 0.905. Overall, candidate prognostic biomarkers were identified in urine specimens from patients with HGSOC and prediction models for the 12-month RFS rate constructed. SIGNIFICANCE: OC is one of the leading causes of death due to gynecological malignancies. HGSOC constitutes one of the most common histologic types of OC with aggressive characteristics, accounting for the majority of advanced cases. In cases where patients with advanced HGSOC potentially face high risk of unfavorable prognosis or disease advancement within a 12-month period, intensive medical monitoring is necessary. In the era of precision cancer medicine, accurate prediction of prognosis or 12-month RFS rate is critical for distinguishing patient groups requiring heightened surveillance. Patients could significantly benefit from timely modifications to treatment regimens based on the outcomes of clinical monitoring. Urine is an ideal resource for disease surveillance purposes due to its easy accessibility. Furthermore, molecules excreted in urine are less complex and more stable than those in other liquid samples. In the current study, we identified candidate prognostic biomarkers in urine specimens from patients with HGSOC and constructed prediction models for the 12-month RFS rate.

N-Glycome changes reflecting resistance to platinum-based chemotherapy in ovarian cancer

A number of studies have reported aberrant glycosylation in connection with malignancy. Our investigation further expands on this topic through the examination of N-glycans, which could be associated with the resistance of advanced stage, high-grade non-mucinous ovarian cancer to platinum/taxane based chemotherapy. We used tissue samples of 83 ovarian cancer patients, randomly divided into two independent cohorts (basic and validation). Both groups involved either cases with/without postoperative tumor residue or the cases determined either resistant or sensitive to this chemotherapy. In the validation cohort, preoperative serum samples were also available. N-glycans released from tumors and sera were permethylated and analyzed by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). The MS analysis yielded a consecutive detection of 68 (tissue) and 63 (serum) N-glycan spectral signals. Eight of these were found to be differentially abundant in tissues of both independent cohorts including the cases with a postoperative cancer residue. One of these glycans was detected as differentially abundant in sera of the validation cohort. No statistically significant differences in intensities due to the same N-glycans were found in the cases without postoperative macroscopic residues in either the basic or validation cohort. From the biochemical point of view, the statistically significant N-glycans correspond to the structures carrying bisecting (terminal) GlcNAc residue and tetra-antennary structures with sialic acid and/or fucose residues. Among them, six tissue N-glycans could be considered potential markers connected with a resistance to chemotherapy in ovarian cancer patients. The prediction of primary resistance to standard chemotherapy may identify the group of patients suitable for alternative treatment strategies. SIGNIFICANCE: Drug resistance has become a major impediment to a successful treatment of patients with advanced ovarian cancer. The glycomic measurements related to cancer are becoming increasingly popular in identification of the key molecules as potential diagnostic and prognostic indicators. Our report deals with identification of differences in N-glycosylation of proteins in tissue and serum samples from the individuals showing sensitivity or resistance to platinum/taxane-based chemotherapy. The detection sensitivity to chemotherapy is vitally important for these patients.

Screening by Q Exactive liquid chromatography/tandem mass spectrometry identified Choline, 25-hydroxyvitamin D2, and SM(d18:0/16:1(9Z) (OH)) as biomarkers for high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) has a high death rate and poor prognosis. The main causes of poor prognosis are asymptomatic early disease, no effective screening method at present, and advanced disease. Changes in cellular metabolism are characteristic of cancer, and plasma metabolome analysis can be used to identify biomarkers. In this study, we used Q Exactive liquid chromatography tandem mass spectrometry (LC-MS/MS, QE) to compare the differentiation between plasma samples (22 HGSOC samples and 22 normal samples). In total, we detected 124 metabolites, and an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model was useful to distinguish HGSOC patients from healthy controls. Choline, 25-hydroxyvitamin D2, and sphingomyelin (d18:0/16:1(9Z) (OH))/SM(d18:0/16:1(9Z) (OH)) showed significantly differential plasma levels in HGSOC patients under the conditions of variable importance in projection (VIP) > 1, p  1, p < 0.05 using Student's t-test, and fold change (FC) ≥ 1.5 or ≤ 0.667. Metabolic pathway analysis can provide valuable information to enhance the understanding of the underlying pathophysiology of HGSOC. In conclusion, the Q Exactive LC/MS/MS method validation-based plasma metabolomics approach may have potential as a convenient screening method for HGSOC and may be a method to monitor tumor recurrence in patients with HGSOC after surgery.

Proteomic insight towards key modulating proteins regulated by the aryl hydrocarbon receptor involved in ovarian carcinogenesis and chemoresistance

Gynecological malignancies pose a severe threat to female lives. Ovarian cancer (OC), the most lethal gynecological malignancy, is clinically presented with chemoresistance and a higher relapse rate. Several studies have highly correlated the incidence of OC to exposure to environmental pollutants, such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), a process mainly mediated through activating the aryl hydrocarbon receptor (AhR). We have previously reported that exposure of OC cells to TCDD, an AhR activator, significantly modulated the expression of several genes that play roles in stemness and chemoresistance. However, the effect of AhR activation on the whole OC cell proteome aiming at identifying novel druggable targets for both prevention and treatment intervention purposes remains unrevealed. For this purpose, we conducted a comparative proteomic analysis of OC cells A2780 untreated/treated with TCDD for 24 h using a mass spectrometry-based label-free shotgun proteomics approach. The most significantly dysregulated proteins were validated by Western blot analysis. Our results showed that upon AhR activation by TCDD, out of 2598 proteins identified, 795 proteins were upregulated, and 611 were downregulated. STRING interaction analysis and KEGG-Reactome pathway analysis approaches identified several significantly dysregulated proteins that were categorized to be involved in chemoresistance, cancer progression, invasion and metastasis, apoptosis, survival, and prognosis in OC. Importantly, selected dysregulated genes identified by the proteomic study were validated at the protein expression levels by Western blot analysis. In conclusion, this study provides a better understanding of the the cross-talk between AhR and several other molecular signaling pathways and the role and involvement of AhR in ovarian carcinogenesis and chemoresistance. Moreover, the study suggests that AhR is a potential therapeutic target for OC prevention and maintenance. SIGNIFICANCE: To our knowledge, this is the first study that investigates the role and involvement of AhR and its regulated genes in OC by performing a comparative proteomic analysis to identify the critical proteins with a modulated expression upon AhR activation. We found AhR activation to play a tumor-promoting and chemoresistance-inducing role in the pathogenesis of OC. The results of our study help to devise novel therapeutics for better management and prevention and open the doors to finding novel biomarkers for the early detection and prognosis of OC.

Quantitative proteomic analysis of cervical cancer based on TMT-labeled quantitative proteomics

Cervical cancer is the second most common gynecological malignancy, which immensely threatens the well-being of women. However, the pathogenesis of cervical cancer is still unclear. Using tandem mass tags-labeled quantitative proteomic technology and bioinformatics tools, we analyzed the exfoliated cervical cells from the normal and cervical cancer groups to establish a cancer-specific protein profile, thereby identifying key proteins related to cervical oncogenesis. When compared with the normal group, a total of 351 differentially expressed proteins were identified in the cervical cancer group, including 247 up-regulated and 104 down-regulated proteins. Gene ontology function annotation revealed that the differentially expressed proteins were mainly involved in the single-multicellular organism process, multicellular organismal process, and negative regulation of biological process. These proteins were discerned to play a role in the extracellular membrane-bounded organelle, exosome of cell components, protein binding, structural molecule activity, and enzyme binding of molecular functions. The results of Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment proved that these differentially expressed proteins were mainly involved in PI3K - Akt, ECM-receptor interaction, complement and coagulation cascades, and other signaling pathways. Particularly, peroxiredoxin-2 may be involved in cervical tumor oncogenesis through inhibition of apoptosis signaling. SIGNIFICANCE: In this study, we determined that the proteins of the cervical cancer group exhibited qualitative and quantitative changes, and a total of 351 differentially expressed proteins were identified. The functions and signaling pathways of these differentially expressed proteins have laid a theoretical foundation for elucidating the molecular mechanism of cervical cancer.

Lipid metabolites abnormally expressed in pelvic fluid as potential biomarkers for ovarian cancer: A case-control study

Ovarian cancer is insidious and usually detected in advanced stages of the disease. As the ovaries are pelvic organs, changes in their pelvic fluid metabolites may be associated with ovarian cancer. Metabolomic changes in the pelvic fluid were detected using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in patients with ovarian cancer, ovarian cysts and uterine fibroids. Area under the curve (AUC) analysis was used to assess the diagnostic performance of lipid metabolites and blood tumor indices. The Pearson correlation algorithm was used to analyze the correlation between clinical characteristics and lipid metabolites in ovarian cancer patients. There were 24 lipid metabolites significantly changed in the pelvic fluid of ovarian cancer patients (p  0.8, with palmitoylcarnitine reaching a high of 0.942. In addition, we found that some lipid metabolites were significantly associated with the clinical stage, abdominal water volume, lymphatic metastasis, and recurrence (p  0.5). Levels of specific lipid metabolites are potential biomarkers of ovarian cancer and may play a key role in the early diagnosis and prognostic assessment of ovarian cancer. Our results showed that pelvic metabolites, especially some lipid metabolites, play an important role in the diagnosis of ovarian cancer. Meanwhile, partial lipid metabolites were closely associated with the clinical presentation and prognosis of patients with ovarian cancer. We believe that our study makes a significant contribution to the literature because it provides a potential approach that is more effective for ovarian cancer detection.

Comprehensive serum proteomic analysis in early endometrial cancer

Endometrial cancer is the most common gynecologic cancer and yet much is still unknown about this disease. Our goal was to identify unique biomarkers of disease by performing a comprehensive proteomic analysis of early stage, low-grade endometrial cancer through analysis of serum collected from patients pre- and post-definitive surgery. We used mass spectrometry (MS)-based proteomics to identify serum proteins from these patients. Serum samples from women undergoing hysterectomy with bilateral salpingo-oophorectomy for benign reasons served as control samples for the correlative studies. We then correlated our findings with The Cancer Genome Atlas (TCGA) database for additional confirmation. The Ingenuity Pathway Analysis of proteins that were differentially expressed in endometrial cancer showed increased cell survival and decreased organismal death, the most common hallmarks of cancer. We identified over expression of FAM83D (family with sequence similarity 83, member D) in the serum of patients with early stage low-grade endometrial cancer and verified the same in the endometrial cancer cell lines and patient tumors. We also confirmed our hypothesis that FAM83D may serve as a biomarker for endometrial cancer in a cohort of patients with endometrial cancer from The Cancer Genome Atlas (TCGA) project. Comprehensive proteomic analysis is a feasible strategy for potential biomarker identification. Using this technique, FAM83D was identified as a candidate biomarker in early endometrial cancer in our patient samples and was not present in benign control samples. FAM83D has been associated with poor clinical outcomes in several human malignancies. Our manuscript describes an alternative approach to comprehensive protein analysis in a model pre and post tumor removal for a sample of patients with early endometrial cancer. The model is innovative and the findings of over expression FAM83D in this population of early cancer may be useful in the study of a disease where there are few biomarkers or targetable therapies.