Identification and validation of a prognostic proteomic signature for cervical cancer

Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells

Cervical cancer (CC) is a major global health problem and leading cause of cancer deaths among women worldwide. Early detection through screening programs has reduced mortality; however, screening compliance remains low. Identifying non-invasive biomarkers through proteomics for diagnosis and monitoring response to treatment could improve patient outcomes. Here we review recent proteomics studies which have uncovered biomarkers and potential drug targets for CC. Additionally, we explore into the role of cervical cancer stem cells and their potential implications in driving CC progression and therapy resistance. Although challenges remain, proteomics has the potential to revolutionize the field of cervical cancer research and improve patient outcomes.

Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts

Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling. However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. Our approach selected predictors regardless of their pathway membership and optimally combined their RPPA measurements into a weighted risk score. Performance was assessed and compared to that of the published signatures using two unbiased approaches: 1) 10 iterations of threefold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan-Meier method) between predictor-defined high and low risk groups; and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Here, we demonstrate strong stratification of 445 renal clear cell carcinoma tumors from The Cancer Genome Atlas (TCGA) into high and low risk groups using LASSO regression on RPPA data. Median cross-validated difference in 5-year overall survival was 32.8%, compared to 25.2% using a published receptor tyrosine kinase (RTK) prognostic signature (median hazard ratios of 3.3 and 2.4, respectively). Applicability and performance of our approach was demonstrated in three additional TCGA cohorts: ovarian serous cystadenocarcinoma (OVCA), sarcoma (SARC), and cutaneous melanoma (SKCM). The data-driven LASSO-based approach is versatile and well-suited for discovery of new protein/disease associations.

Anti-Programmed Death Protein-1 (PD-1) Antibody Combined with Paclitaxel Exert Anti-Cancer Effect on Cervical Cancer Cells

The therapeutic effect of combined drugs on cervical cancer has been confirmed. Whether anti-PD-1 antibody combined with paclitaxel mediates the PI3K-Akt pathway to regulate cervical cancer still requires further research. 20 nude mice received subcutaneous administration of Hela cells to establish cervical cancer model which was then assigned into blank control group, control group A (PD-1 antibody (5 mg/ kg) administration), control group B (paclitaxel), and observation group (PD-1 antibody combined with paclitaxel) followed by analysis of cell proliferation, apoptosis, expression of PI3K-Akt signaling proteins and mRNAs. Observation group had lowest tumor size, highest cell proliferation inhibition rate and apoptosis, which were all reversed in blank group with a largest tumor size, lowest cell proliferation inhibition rate and cell apoptosis. There were no differences between control group A and control group B (P > 0.05). The expressions of PI3K, Akt, p53, and p21 proteins were lowest in observation group and highest in blank group. In addition, control group showed no difference to control group B (P > 0.05). In conclusion, anti-PD-1 antibody combined with paclitaxel inhibits PI3K-Akt signaling activity, thereby downregulating PI3K, Akt, p53, and p21 protein, controlling cervical cancer cell division, promoting cell apoptosis, and exerting anti-tumor effects.

Bone Marrow Mesenchymal Stem Cells (BMSCs)-Triggered Up-Regulation of miR-198 Impedes the Aggressive Migration and Invasion of Cervical Cancer Cells

As a subset of RNAs without protein-coding function, short non-coding RNAs (microRNAs) are reported to contribute to the progress of multiple disorders. Nevertheless, the precise function of miR-198 in human cervical cancer is still an open question. RNA sequencing between cervical cancer cell lines and normal cervical epithelial cells identified CCAR1 to be highly expressed in cervical cancer. Cells were transfected with si-CCAR1 followed by analysis of cell behaviors using clonogenic assay and transwell migrating assay. The binding of miR-198 and CCAR1 was verified by a dual-luciferase reporter gene experiment. CCAR1 was highly expressed in cervical cancer tissues and cell lines and associated with tumor staging. Knockdown of CCAR1 restrained the malignant phenotypes of cervical cancer cells. CCAR1 was a target of miR198. Co-culture with BMSCs upregulated miR-198 expression, resulting in impediment of the aggressive phenotypes of cervical cancer cells, which was mediated by suppression of CCAR1 and release of inflammatory factors. In conclusion, CCAR1 level is increased in cervical cancer tissues or cell lines. Co-culture of BMSCs can facilitate the proliferating, migrating and invading activities of cervical cancer cells but reduce the release of inflammatory factors which is possibly through manipulating the axis of miR-198/CCAR1.

Contribution of Multiplex Immunoassays to Rheumatoid Arthritis Management: From Biomarker Discovery to Personalized Medicine

Rheumatoid arthritis (RA) is a multifactorial, inflammatory and progressive autoimmune disease that affects approximately 1% of the population worldwide. RA primarily involves the joints and causes local inflammation and cartilage destruction. Immediate and effective therapies are crucial to control inflammation and prevent deterioration, functional disability and unfavourable progression in RA patients. Thus, early diagnosis is critical to prevent joint damage and physical disability, increasing the chance of achieving remission. A large number of biomarkers have been investigated in RA, although only a few have made it through the discovery and validation phases and reached the clinic. The single biomarker approach mostly used in clinical laboratories is not sufficiently accurate due to its low sensitivity and specificity. Multiplex immunoassays could provide a more complete picture of the disease and the pathways involved. In this review, we discuss the latest proposed protein biomarkers and the advantages of using protein panels for the clinical management of RA. Simultaneous analysis of multiple proteins could yield biomarker signatures of RA subtypes to enable patients to benefit from personalized medicine.

Identification of a transcriptomic signature with excellent survival prediction for squamous cell carcinoma of the cervix

Survival for patients with newly diagnosed cervical cancer has not significantly improved over the past several decades. We sought to identify a clinically relevant set of prognostic genes for squamous cell carcinoma of the cervix (SCCC), the most common cervical cancer subtype. Using RNA-sequencing data and survival data from 203 patients in The Cancer Genome Atlas (TCGA), we conducted a series of analyses using different decile cutoffs for gene expression to identify genes that could indicate large and consistent survival differences across different decile cutoffs of gene expression. Those analyses identified 42 high-risk genes. A patient's survivability could be estimated by simply counting the number of high-risk genes with extremely high expression (above the 90^th percentile) or estimating a transcriptomic risk score (TRS) using a machine learning algorithm with 9 of the 42 genes. On multivariate analysis, the significant predictors of mortality included high TRS (HR = 44.8), stage IV (HR = 28.1), intermediate TRS (HR = 4.75), and positive lymph node status (HR = 2.92). Approximately 18% of earlier-stage patients were identified as a poor-prognosis subgroup with high TRS. In patients with SCCC, transcriptomic risk appears to better predict survival than clinical prognostic factors, including stage.

Proteomic and ultrastructural analysis of Clara cell and type II alveolar epithelial cell-type lung cancer cells

BACKGROUND

Currently, the identification of Clara cell and type II alveolar epithelial cell-type cancer cells requires electron microscopy, which is a time-consuming and expensive process involving a complicated tissue sampling procedure. The aim of this study was to identify unique biomarkers for Clara cell and type II alveolar epithelial cell-type lung cancer cells, respectively, with proteomic profiling.

METHODS

Six human lung adenocarcinoma cell lines (A549, NCI-H358, NCI-H1650, HCC827, NCI-H1395, and NCI-H1975) were investigated for their ultrastructural characteristics. The differentially expressed proteins (DEPs) were screened between NCI-H358 cells (Clara cell type) and A549 cells (type II alveolar epithelial cell type) using two-dimensional difference gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS/MS), and then they were validated by western blot. The protein expression levels of endoplasmic reticulum oxidoreductin 1-α (ERO1L), Clara cell 10-kD protein (CC10), and surfactant protein C (SP-C) were also determined in the six cell lines assayed.

RESULTS

NCI-H358 cells featured Clara cell differentiation; A549, NCI-H1975, and HCC827 cells had characteristics of type II alveolar epithelial cells; and NCI-H1395 and NCI-H1650 cells had no differentiation characteristics of any lung adenocarcinoma cell type. Five DEPs including ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1), cytokeratin 19 (CK19), cytokeratin 8 (CK8), ERO1L, and peroxiredoxin 2 (PRDX2) between NCI-H358 and A549 cells were identified for further validation; however, none of them showed suitability as an effective biomarker. Similarly, CC10 and SP-C were not appropriate biomarkers.

CONCLUSIONS

Cytological subtypes of NCI-H1975 and HCC827 cells were identified, but no promising biomarker was discovered in the present study.