Urinary protein biomarker database: a useful tool for biomarker discovery

Comprehensive Identification and Characterization of Human Secretome Based on Integrative Proteomic and Transcriptomic Data

Secreted proteins (SPs) play important roles in diverse important biological processes; however, a comprehensive and high-quality list of human SPs is still lacking. Here we identified 6,943 high-confidence human SPs (3,522 of them are novel) based on 330,427 human proteins derived from databases of UniProt, Ensembl, AceView, and RefSeq. Notably, 6,267 of 6,943 (90.3%) SPs have the supporting evidences from a large amount of mass spectrometry (MS) and RNA-seq data. We found that the SPs were broadly expressed in diverse tissues as well as human body fluid, and a significant portion of them exhibited tissue-specific expression. Moreover, 14 cancer-specific SPs that their expression levels were significantly associated with the patients’ survival of eight different tumors were identified, which could be potential prognostic biomarkers. Strikingly, 89.21% of 6,943 SPs (2,927 novel SPs) contain known protein domains. Those novel SPs we mainly enriched with the known domains regarding immunity, such as Immunoglobulin V-set and C1-set domain. Specifically, we constructed a user-friendly and freely accessible database, SPRomeDB (www.unimd.org/SPRomeDB), to catalog those SPs. Our comprehensive SP identification and characterization gain insights into human secretome and provide valuable resource for future researches.

miR-21-5p, miR-141-3p, and miR-205-5p levels in urine-promising biomarkers for the identification of prostate and bladder cancer

BACKGROUND

Early detection of cancers improves patients' survival and decreases the treatment cost. Unfortunately, the current methods for diagnosis of bladder and prostate cancers, two most common urothelial malignancies, suffer from a low sensitivity and specificity. MicroRNAs, as a group of endogenously produced non-coding RNAs, regulate gene expression and their expression is observed to be altered in many cancers and cancer progression phenomena. The remarkable stability of microRNAs in biofluids and their unique expression pattern in different pathological conditions make them an appealing, noninvasive diagnostic method in cancer diagnosis. Our objective is to identify microRNAs as biomarkers in urine samples of bladder and prostate cancers to improve the existing diagnostic methods in this field.

MATERIALS AND METHODS

In this study, urine samples from 110 men with either bladder (n = 45) or prostate (n = 23) cancer, benign prostatic hyperplasia (n = 22) and healthy controls (n = 20) were collected. qPCR was used to evaluate the expression level of miR-21-5p, miR-141-3p, and miR-205-5p in these samples. The sensitivity and specificity of these microRNAs were determined using ROC curve analysis.

RESULTS

The analysis of the data revealed that miR-21-5p, miR-141-3p, and miR-205-5p are differentially expressed in urine of bladder and prostate cancer patients. All these three microRNAs were upregulated in these samples and they were also able to differentiate benign prostatic hyperplasia from malignant cases. The statistical analyses revealed a good specificity for each individual microRNA.

CONCLUSION

The results show that these three urine-based microRNAs might be a good choice to implement a specific and non-invasive diagnostic tool for bladder and prostate cancer. The expression pattern of all three microRNAs was particularly useful to distinguish benign and invasive tumors in prostate cases. From the patients' perspective the improvement of the diagnostic situation is awaited eagerly.

Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure

Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called "AntiMPmod" which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/).

Exosomes in Cancer Liquid Biopsy: A Focus on Breast Cancer

The important challenge about cancer is diagnosis in primary stages and proper treatment. Although classical clinico-pathological features of the tumor have major prognostic value, the advances in diagnosis and treatment are indebted to discovery of molecular biomarkers and control of cancer in the pre-invasive state. Moreover, the efficiency of available therapeutic options is highly diminished, and chemotherapy is still the main treatment due to lack of enough specific targets. Accordingly, finding the new noninvasive biomarkers for cancer is still an important clinical challenge that is not achieved yet. There are current technologies to screen, diagnose, prognose, and treat cancer, but the limitations of these implements and procedures are undeniable. Liquid biopsy as a noninvasive method has a promising future in the field of cancer, and exosomes as one of the recent areas have drawn much attention. In this review, the potential capability of exosomes is summarized in cancer with the special focus on breast cancer as the second cause of cancer mortality in women all around the world. It discusses reasons to choose exosomes for liquid biopsy and the studies related to different potential biomarkers found in the exosomes. Moreover, exosome studies on milk as a specific biofluid are also discussed. At last, because choosing the method for exosome studies is very challenging, a summary of different techniques is provided.

Proof-of-Concept Workflow for Establishing Reference Intervals of Human Urine Proteome for Monitoring Physiological and Pathological Changes

Urine as a true non-invasive sampling source holds great potential for biomarker discovery. While approximately 2000 proteins can be detected by mass spectrometry in urine from healthy people, the amount of these proteins vary considerably. A systematic evaluation of a large number of samples is needed to determine the range of the variations. Current biomarker studies often measure limited number of urine samples in the discovery phase, which makes it difficult to determine whether proteins differentially expressed between control and disease groups represent actual difference, or are just physiological variations among the individuals, leads to failures in the validation phase with the increased sample numbers. Here, we report a streamlined workflow with capacity of measuring 8 urine proteomes per day at the coverage of >1500 proteins. With this workflow, we evaluated variations in 497 urine proteomes from 167 healthy donors, establishing reference intervals (RIs) that covered urine protein variations. We demonstrated that RIs could be used to monitor physiological changes by detecting transient outlier proteins. Furthermore, we provided a RIs-based algorithm for biomarker discovery and validation to screen for diseases such as cancer. This study provided a proof-of-principle workflow for the use of urine proteome for health monitoring and disease screening.