Discovery of bladder cancer biomarkers in paired pre- and postoperative urine samples.

BACKGROUND: Bladder cancer (BC), a common cancer of the urinary system, has a low mortality but an extremely high recurrence rate. Patients who have undergone initial surgical treatment often undergo frequent prognostic examinations with a substantial burden of discomfort and costs. Urine samples can reflect early disease processes in the urinary system and may be an excellent source of biomarkers. METHODS: In the present study, we used the liquid chromatography with tandem mass spectrometry (LC-MS/MS) to perform proteomic analysis of pre- and postoperative urine samples from patients with stage III BC to identify biomarkers of cancer prognosis. Candidate biomarkers from proteomic analysis were simultaneously validated using western blotting in an independent cohort and immunohistochemical (IHC) staining, combined with gene expression data of BC samples in The Cancer Genome Atlas (TCGA). RESULTS: The comparison of pre- and postoperative urine samples from the same patients led to the discovery of several significantly differentially expressed proteins, whose functions could be closely related to the occurrence and development of BC. We confirmed a representative group of candidate biomarker molecules, such as cadherin-related family member 2 (CDHR2), heat shock protein beta-1 (HSP27), and heterogeneous nuclear ribonucleoproteins A2/B1 (HNRNPA2B1). CONCLUSIONS: The candidate biomarker molecules can distinguish between pre- and postoperative urine samples, and alterations in their expression levels are significantly associated with recurrence rates in patients with BC. Therefore, these molecules may become useful biomarkers for the monitoring and prognosis of BC.

[Research progress of feature selection and machine learning methods for mass spectrometry-based protein biomarker discovery].

With the development of mass spectrometry technologies and bioinformatics analysis algorithms, disease research-driven human proteome project (HPP) is advancing rapidly. Protein biomarkers play critical roles in clinical applications and the biomarker discovery strategies and methods have become one of research hotspots. Feature selection and machine learning methods have good effects on solving the "dimensionality" and "sparsity" problems of proteomics data, which have been widely used in the discovery of protein biomarkers. Here, we systematically review the strategy of protein biomarker discovery and the frequently-used machine learning methods. Also, the review illustrates the prospects and limitations of deep learning in this field. It is aimed at providing a valuable reference for corresponding researchers.

Biological roles of filamin a in prostate cancer cells

ABSTRACT Objective This study aims to investigate the association of filamin A with the function and morphology of prostate cancer (PCa) cells, and explore the role of filamin A in the development of PCa, in order to analyze its significance in the evolvement of PCa. Materials and Methods A stably transfected cell line, in which filamin A expression was suppressed by RNA interference, was first established. Then, the effects of the suppression of filamin A gene expression on the biological characteristics of human PCa LNCaP cells were observed through cell morphology, in vitro cell growth curve, soft agar cloning assay, and scratch test. Results A cell line model with a low expression of filamin A was successfully constructed on the basis of LNCaP cells. The morphology of cells transfected with plasmid pSilencer-filamin A was the following: Cells were loosely arranged, had less connection with each other, had fewer tentacles, and presented a fibrous look. The growth rate of LNCap cells was faster than cells transfected with plasmid pSilencer-filamin A (P <0.05). The clones of LNCap cells in the soft agar cloning assay was significantly fewer than that of cells stably transfected with plasmid pSilencer-filamin A (P <0.05). Cells stably transfected with plasmid pSilencer-filamin A presented with a stronger healing and migration ability compared to LNCap cells (healing rate was 32.2% and 12.1%, respectively; P <0.05). Conclusion The expression of the filamin A gene inhibited the malignant development of LNCap cells. Therefore, the filamin A gene may be a tumor suppressor gene.

Biological roles of filamin a in prostate cancer cells.

OBJECTIVE: This study aims to investigate the association of filamin A with the function and morphology of prostate cancer (PCa) cells, and explore the role of filamin A in the development of PCa, in order to analyze its significance in the evolvement of PCa. MATERIALS AND METHODS: A stably transfected cell line, in which filamin A expression was suppressed by RNA interference, was first established. Then, the effects of the suppression of filamin A gene expression on the biological characteristics of human PCa LNCaP cells were observed through cell morphology, in vitro cell growth curve, soft agar cloning assay, and scratch test. RESULTS: A cell line model with a low expression of filamin A was successfully constructed on the basis of LNCaP cells. The morphology of cells transfected with plasmid pSilencer-filamin A was the following: Cells were loosely arranged, had less connection with each other, had fewer tentacles, and presented a fibrous look. The growth rate of LNCap cells was faster than cells transfected with plasmid pSilencer-filamin A (P<0.05). The clones of LNCap cells in the soft agar cloning assay was significantly fewer than that of cells stably transfected with plasmid pSilencer-filamin A (P<0.05). Cells stably transfected with plasmid pSilencer-filamin A presented with a stronger healing and migration ability compared to LNCap cells (healing rate was 32.2% and 12.1%, respectively; P<0.05). CONCLUSION: The expression of the filamin A gene inhibited the malignant development of LNCap cells. Therefore, the filamin A gene may be a tumor suppressor gene.

[A new bioinformatics approach for prediction of potential tumor neoantigens based on the cancer genome atlas dataset].

Tumor-specific gene mutations might generate suitable neoepitopes for cancer immunotherapy that are highly immunogenic and absent in normal tissues. The high heterogeneity of the tumor genome poses a big challenge for precision cancer immunotherapy. Mutations characteristic of each tumor can help to distinguish it from other tumors. Based on these mutations' characteristic, it is possible to develop immunotherapeutic strategies for specific tumors. In this study, a tumor neoantigen prediction scheme was proposed, in which both the intracellular antigen presentation process and the ability to bind with extracellular MHC molecule were taken into consideration. The overall design is meritorious and may help reduce the cost for validation experiments compared with conventional methods. This strategy was tested with several cancer genome datasets in the TCGA database, and a number of potential tumor neoantigens were predicted for each dataset. These predicted neoantigens showed tumor type specificity and were found in 20% to 70% of cancer patients. This scheme might prove useful clinically in future.