Proteome profiling of low grade serous ovarian cancer

Identification and validation of immunity- and disulfidptosis-related genes signature for predicting prognosis in ovarian cancer

Background

Ovarian cancer (OC) ranks as the fifth most prevalent neoplasm in women and exhibits an unfavorable prognosis. To improve the OC patient's prognosis, a pioneering risk signature was formulated by amalgamating disulfidptosis-related genes.

Methods

A comparative analysis of OC tissues and normal tissues was carried out, and differentially expressed disulfidptosis-related genes (DRGs) were found using the criteria of |log2 (fold change) | > 0.585 and adjusted P-value <0.05. Subsequently, the TCGA training set was utilized to create a prognostic risk signature, which was validated by employing both the TCGA testing set and the GEO dataset. Moreover, the immune cell infiltration, mutational load, response to chemotherapy, and response to immunotherapy were analyzed. To further validate these findings, QRT-PCR analysis was conducted on ovarian tumor cell lines.

Results

A risk signature was created using fourteen differentially expressed genes (DEGs) associated with disulfidptosis, enabling the classification of ovarian cancer (OC) patients into high-risk group (HRG) and low-risk group (LRG). The HRG exhibited a lower overall survival (OS) compared to the LRG. In addition, the risk score remained an independent predictor even after incorporating clinical factors. Furthermore, the LRG displayed lower stromal, immune, and estimated scores compared to the HRG, suggesting a possible connection between the risk signature, immune cell infiltration, and mutational load. Finally, the QRT-PCR experiments revealed that eight genes were upregulated in the human OC cell line SKOV3 compared with the human normal OC line IOSE80, while six genes were down-regulated.

Conclusions

A fourteen-biomarker signature composed of disulfidptosis-related genes could serve as a valuable risk stratification tool in OC, facilitating the identification of patients who may benefit from individualized treatment and follow-up management.

Proteome profiling of ductal carcinoma in situ

BACKGROUND AND AIM

DCIS is the most common type of non-invasive breast cancer, accounting for about 15 to 30%. Proteome profile is used to detect biomarkers in the tissues of breast cancer patients by mass spectrometry. This study aimed to obtain the expression profile of DCIS proteome, and the expression profile of invasive biomarkers, and finally to introduce a dedicated biomarker panel to facilitate the prognosis and early detection for in situ breast cancer patients.

METHODS AND MATERIALS

In this study, 10 patients with breast cancer (DCIS) were studied. Benign (marginal) and cancerous tissue samples were obtained from patients for proteomics experiments. Initially, all tissue proteins were extracted using standard methods, and the proteins were separated using two-dimensional electrophoresis. Then, the expression amount of the extracted proteins was determined by ITRAQ. The data were analysed by R software, and gene ontology was utilised for describing the protein in detail.

RESULTS

30 spots on gel electrophoresis were found in the tumor tissue group (sample), and 15 spots in the margin group (control) with P < 0.05. Healthy and cancerous tissue gels showed that 5 spots had different expression. VWF, MMP9, ITGAM, MPO and PLG protein spots were identified using the site www.ebi.ac.uk/IPI. Finally, protein biomarkers for breast tumor tissue with margin were introduced with the names of P04406, P49915, P05323, P06733, and P02768.

DISCUSSION

There are 5 critical proteins in inducing cancer pathways especially complement and coagulation cascades. The hall markers of a healthy cell to be cancerous are proliferation, invasion, angiogenesis, and changes in the immune system. Hence, regulation of protein plays a key role in developing recurrence to breast cancer in margins.

Clinical Performance of a Multivariate Index Assay in Detecting Early-Stage Ovarian Cancer in Filipino Women

This study evaluated the clinical performance and overall utility of a multivariate index assay in detecting early-stage ovarian cancer in a Filipino population. This is a prospective cohort study among Filipino women undergoing assessment for an ovarian mass in a tertiary center. Patients diagnosed with early-stage ovarian cancer and who underwent a physical examination before level III specialist ultrasonographic and Doppler evaluation, multivariate index assay (MIA2G), and surgery for an adnexal mass were included in this study. Ovarian tumors were classified as high-risk for malignancy based on the IOTA-LR2 score. The ovarian imaging and biomarker results were correlated with the reference standard: surgico-pathologic findings. The MIA2G exhibited the best overall performance among individual classifiers with a sensitivity of 91.7% and NPV of 84.7%, with a concomitant higher sensitivity in early-stage disease, whether as an individual classifier (93.5%) or in serial combination with ultrasound (85.5%). The performance of biomarkers (specificity, positive predictive values, and AUROC) such as MIA2G and CA-125 significantly improved when combined with an ultrasound risk scoring approach (p < 0.01). MIA2G showed a higher sensitivity for detecting lesions among EOC and late-stage ovarian cancers than otherwise. The application of biomarkers for evaluating ovarian masses in our local setting is secondary to ultrasound but adopting multivariate index assays rather than CA-125 would increase the detection of early-stage ovarian cancers regardless of menopausal status. This is most relevant in areas where level III sonographers or gynecologic oncologists are limited and preoperative referrals to these specialists can improve the survival of our patients.

Malignant Ascites in Ovarian Cancer: Cellular, Acellular, and Biophysical Determinants of Molecular Characteristics and Therapy Response

Ascites refers to the abnormal accumulation of fluid in the peritoneum resulting from an underlying pathology, such as metastatic cancer. Among all cancers, advanced-stage epithelial ovarian cancer is most frequently associated with the production of malignant ascites and is the leading cause of death from gynecologic malignancies. Despite decades of evidence showing that the accumulation of peritoneal fluid portends the poorest outcomes for cancer patients, the role of malignant ascites in promoting metastasis and therapy resistance remains poorly understood. This review summarizes the current understanding of malignant ascites, with a focus on ovarian cancer. The first section provides an overview of heterogeneity in ovarian cancer and the pathophysiology of malignant ascites. Next, analytical methods used to characterize the cellular and acellular components of malignant ascites, as well the role of these components in modulating cell biology, are discussed. The review then provides a perspective on the pressures and forces that tumors are subjected to in the presence of malignant ascites and the impact of physical stress on therapy resistance. Treatment options for malignant ascites, including surgical, pharmacological and photochemical interventions are then discussed to highlight challenges and opportunities at the interface of drug discovery, device development and physical sciences in oncology.

Comparative sera proteomics analysis of differentially expressed proteins in oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) has increased in incidence from 1990 to 2017, especially in South and Southeast Asia. It is often diagnosed at an advanced stage with a poor prognosis. Therefore, early detection of OSCC is essential to improve the prognosis of OSCC. This study aims to identify the differentially expressed serum proteins as potential biomarkers for oral squamous cell carcinoma (OSCC).

Methods

Comparative proteomics profiling of serum samples from OSCC patients, oral potentially malignant disorder (OPMD) patients, and healthy individuals were performed using two-dimensional gel electrophoresis (2-DE) coupled with mass spectrometry (MS) (n = 60) and bioinformatics analysis. The enzyme-linked immunosorbent assay (ELISA) (n = 120) and immunohistochemistry (IHC) (n = 70) were used to confirm our findings.

Results

The 2-DE analysis revealed that 20 differentially expressed proteins were detected in OPMD and OSCC (p < 0.05). Bioinformatics analysis indicated that the activation of classical complement, liver X receptor/retinoid X receptor (LXR/RXR) activation, and acute phase response signaling pathway are associated with the development and progression of OSCC. Most of the detected proteins are acute-phase proteins and were related to inflammation and immune responses, including apolipoprotein A-I (APOA1), complement C3 (C3), clusterin (CLU), and haptoglobin (HP). The expression levels of CLU and HP in ELISA are consistent with the findings from the 2-DE analysis, except for the mean serum level of HP in OPMD, whereby it was slightly higher than that in control. IHC results demonstrated that CLU and HP are significantly decreased in OSCC tissues.

Conclusion

Decreased expression of CLU and HP could serve as complementary biomarkers of OSCC. These proteins may assist in predicting the outcomes of OSCC patients. However, a larger cohort is needed for further investigation.

Proteomic analysis of human cervical adenocarcinoma mucus to identify potential protein biomarkers

Background

Cervical cancer is the most common gynecological cancer, encompassing cervical squamous cell carcinoma, adenocarcinoma, and other epithelial tumors. There are many diagnostic methods to detect cervical cancers but no precision screening tool for cervical adenocarcinoma at present.

Material and methods

The cervical mucus from three normal cervices (Ctrl), three endocervical adenocarcinoma (EA), and three cervical adenocarcinoma in situ (AIS) was collected for proteomic analysis. The proteins were screened using liquid chromatography-mass spectrometry analysis (LC-MS). The biological function of the differently expressed proteins were predicted by Gene Ontology (GO).

Results

A total of 711 proteins were identified, including 237 differently expressed proteins identified in EA/Ctrl comparison, 256 differently expressed proteins identified in AIS/Ctrl comparison, and 242 differently expressed proteins identified in AIS/EA comparison (up-regulate ≥ 1.5 or down-regulate ≤ 0.67). Functional annotation was performed using GO analysis on 1,056 differently expressed proteins to identify those that may impact cervical cancer, such as heme protein myeloperoxidase, which is involved in the immune process, and APOA1, which is associated with lipid metabolism.

Conclusion

We used proteomic analysis to screen out differently expressed proteins from normal cervical mucus and cervical adenocarcinoma mucus samples. These differently expressed proteins may be potential biomarkers for the diagnosis and treatment of cervical adenocarcinoma but require additional study.