Comparison of mRNA, Protein, and Urinary Nucleic Acid Levels of S100A8 and S100A9 between Prostate Cancer and BPH

CircRNA circ_0006156 inhibits the metastasis of prostate cancer by blocking the ubiquitination of S100A9

Circular RNAs (circRNAs) have been demonstrated to play vital roles in cancer development and progression. However, studies on the association between circRNAs and prostate cancer (PCa) are still lacking. CircRNA sequencing of two pairs of PCa tissues and adjacent normal tissues was conducted in the present study, and qRT-PCR was performed to verify the results. Functional experiments were performed to investigate cellular functions after specific changes. Mass spectrometry analysis after RNA pull-down experiments and Co-IP assays were further conducted. Downstream target proteins were predicted via online databases and detected in vitro by Western blot analysis and in vivo by immunohistochemistry. Hsa_circ_0006156 (subsequently named circ_0006156) expresses at low levels in both PCa tissues and cells, and it significantly inhibits the migration and invasion of PCa cells. Circ_0006156 binds to and blocks the ubiquitination of S100A9. Moreover, functional assays revealed that circ_0006156 represses the malignant progression of PCa by binding to S100A9. Finally, in vivo experiments showed that circ_0006156 suppresses PCa migration and invasion by increasing S100A9, revealing circ_0006156 as a potential novel effective target for PCa treatment.

Prostate Cancer Biomarkers: From diagnosis to prognosis and precision-guided therapeutics

Prostate cancer (PCa) is one of the most commonly diagnosed malignancies and among the leading causes of cancer-related death worldwide. It is a highly heterogeneous disease, ranging from remarkably slow progression or inertia to highly aggressive and fatal disease. As therapeutic decision-making, clinical trial design and outcome highly depend on the appropriate stratification of patients to risk groups, it is imperative to differentiate between benign versus more aggressive states. The incorporation of clinically valuable prognostic and predictive biomarkers is also potentially amenable in this process, in the timely prevention of metastatic disease and in the decision for therapy selection. This review summarizes the progress that has so far been made in the identification of the genomic events that can be used for the classification, prediction and prognostication of PCa, and as major targets for clinical intervention. We include an extensive list of emerging biomarkers for which there is enough preclinical evidence to suggest that they may constitute crucial targets for achieving significant advances in the management of the disease. Finally, we highlight the main challenges that are associated with the identification of clinically significant PCa biomarkers and recommend possible ways to overcome such limitations.

Therapeutic prognosis of prostate cancer using surface-enhanced Raman scattering of patient urine and multivariate statistical analysis

Surface-enhanced Raman scattering (SERS) is highly sensitive and label-free analytical technique based on Raman spectroscopy aided by field-multiplying plasmonic nanostructures. We report the use of SERS measurements of patient urine in conjunction with biostatistical algorithms to assess the treatment response of prostate cancer (PCa) in 12 recurrent (Re) and 63 nonrecurrent (NRe) patient cohorts. Multiple Raman spectra are collected from each urine sample using monodisperse silver nanoparticles (AgNPs) for Raman signal enhancement. Genetic algorithms-partial least squares-linear discriminant analysis (GA-PLS-LDA) was employed to analyze the Raman spectra. Comprehensive GA-PLS-LDA analyses of these Raman spectral features (p = 3.50 × 10^-16 ) yield an accuracy of 86.6%, sensitivity of 86.0%, and specificity 87.1% in differentiating the Re and NRe cohorts. Our study suggests that SERS combined with multivariate GA-PLS-LDA algorithm can potentially be used to detect and monitor the risk of PCa relapse and to aid with decision-making for optimal intermediate secondary therapy to recurred patients.

Prognostic Role of S100A8 in Human Solid Cancers: A Systematic Review and Validation

Background

S100A8 plays a key role in many cellular processes and is highly expressed in various solid cancers. However, the prognostic role of S100A8 has not been well defined. Therefore, we conducted a quantitative meta-analysis to investigate whether or not S100A8 could be used as a prognostic biomarker in solid tumors.

Methods

PubMed, Web of Science, Embase, and Cochrane library were searched to acquire relevant studies that evaluated the association between expression of S100A8 and prognosis of cancer patients. Pooled hazard ratios (HRs) with their corresponding 95% confidence intervals (CIs) were extracted to evaluate the association between S100A8 overexpression and Overall Survival (OS), Disease-Free Survival (DFS), Recurrence-Free Survival (RFS), and Progression-Free Survival (PFS). The expression of S100A8 was also validated by Flow cytometry, immunohistochemistry (IHC), and western blot.

Results

A total of 2,817 patients from 13 independent studies, ranging from 43 to 1,117 patients in size, were statistically analyzed. Our results indicated that a high level of S100A8 expression was significantly associated with poor OS, poor DFS, and poor PFS/RFS. In term of clinical pathological characteristics, a high expression level of S100A8 was significantly associated with differentiation grades, lymphatic metastasis, ER statue, and PR statue. The validation studies showed that the expression of S100A8 was at high levels in MDA-MB-231 (79.7%), MDA-MB-453 (89.2%), HTB-9 (70.2%), and T24 (53.3%) cells and it was higher in breast cancer tissue and bladder cancer tissue than their corresponding para-carcinoma tissue.

Conclusions

S100A8 overexpression was significantly associated with poor clinical prognosis in cancer patients. S100A8 is potential a prognostic biomarker in breast cancer and bladder cancer. More well-designed studies with adequate prognostic data are needed to confirm the prognostic role of S100A8 revealed in this study.

ADAPT identifies an ESCRT complex composition that discriminates VCaP from LNCaP prostate cancer cell exosomes

Libraries of single-stranded oligodeoxynucleotides (ssODNs) can be enriched for sequences that specifically bind molecules on naïve complex biological samples like cells or tissues. Depending on the enrichment strategy, the ssODNs can identify molecules specifically associated with a defined biological condition, for example a pathological phenotype, and thus are potentially useful for biomarker discovery. We performed ADAPT, a variant of SELEX, on exosomes secreted by VCaP prostate cancer cells. A library of ∼10¹¹ ssODNs was enriched for those that bind to VCaP exosomes and discriminate them from exosomes derived from LNCaP prostate cancer cells. Next-generation sequencing (NGS) identified the best discriminating ssODNs, nine of which were resynthesized and their discriminatory ability confirmed by qPCR. Affinity purification with one of the sequences (Sequence 7) combined with LC–MS/MS identified its molecular target complex, whereof most proteins are part of or associated with the multiprotein ESCRT complex participating in exosome biogenesis. Within this complex, YBX1 was identified as the directly-bound target protein. ADAPT thus is able to differentiate exosomes from cancer cell subtypes from the same lineage. The composition of ESCRT complexes in exosomes from VCaP versus LNCaP cells might constitute a discriminatory element between these prostate cancer subtypes.

Biomarkers That Differentiate Benign Prostatic Hyperplasia from Prostate Cancer: A Literature Review

Prediction of prostate cancer in primary care is typically based upon serum total prostate-specific antigen (tPSA) and digital rectal examination results. However, these tests lack sensitivity and specificity, leading to over-diagnosis of disease and unnecessary, invasive biopsies. Therefore, there is a clinical need for diagnostic tests that can differentiate between benign conditions and early-stage malignant disease in the prostate. In this review, we evaluate research papers published from 2009 to 2019 reporting biomarkers that identified or differentiated benign prostatic hyperplasia (BPH) from prostate cancer. Our review identifies hundreds of potential biomarkers in urine, serum, tissue, and semen proposed as useful targets for differentiating between prostate cancer and BPH patients. However, it is still not apparent which of these candidate biomarkers are most useful, and many will not progress beyond the discovery stage unless they are properly validated for clinical practice. We conclude that this validation will come through the use of multivariate panels which can assess the value of biomarker candidates in combination with clinical parameters as part of a risk prediction calculator. Implementation of such a model will help clinicians stratify patients with prostate cancer symptoms in primary care, with tangible benefits for both the patient and the health service.

Down-Regulation of S100A8 is an Independent Predictor of PSA Recurrence in Prostate Cancer Treated by Radical Prostatectomy

Dysregulation of S100A8 is described in many different human tumor types, but its role in prostate cancer is unknown. To evaluate the clinical relevance of S100A8 expression in prostate cancer, a tissue microarray containing 13,665 tumors was analyzed by immunohistochemistry. Cytoplasmic S100A8 staining was compared to prostate cancer phenotype, patient prognosis and molecular features including TMPRSS2:ERG fusion status and deletions of PTEN, 3p, 5q and 6q. S100A8 immunostaining was typically seen in normal prostate tissue but lost in 60% of 9786 interpretable prostate cancers. In the remaining tumors, S100A8 was considered weak in 17.9%, moderate in 17.8% and strong in 5.4% of cases. Loss of S100A8 expression was linked to advanced tumor stage, high Gleason grade, positive nodal status, positive surgical margin and high preoperative PSA (P < .0001 each). In addition, loss of S100A8 expression was associated with TMPRSS2:ERG fusions (P < .0001), deletions of PTEN, 3p, and 6q (P < .005), and a high number of genomic deletions per tumor (P = .0009). Absence of S100A8 immunostaining was also linked to an elevated risk for early PSA recurrence (P < .0001). In a multivariate analysis limited to features that are preoperatively available, the prognostic impact of S100A8 expression (P < .0001) was independent of clinical stage, Gleason grade, and serum PSA level (P < .0001). Taken together, the results of our study demonstrate that complete loss of S100A8 expression is linked to adverse tumor features and predicts early biochemical recurrence in prostate cancer. S100A8 measurement, either alone or in combination might be of clinical utility in prostate cancers.

Identification of an eight-gene signature for survival prediction for patients with hepatocellular carcinoma based on integrated bioinformatics analysis

Background

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related death worldwide. Despite recent advances in imaging techniques and therapeutic intervention for HCC, the low overall 5-year survival rate of HCC patients remains unsatisfactory. This study aims to find a gene signature to predict clinical outcomes in HCC.

Methods

Bioinformatics analysis including Cox’s regression analysis, Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analysis and the random survival forest algorithm were performed to mine the expression profiles of 553 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public database.

Results

We selected a signature comprising eight protein-coding genes (DCAF13, FAM163A, GPR18, LRP10, PVRIG, S100A9, SGCB, and TNNI3K) in the training dataset (AUC = 0.77 at five years, n = 332). The signature stratified patients into high- and low-risk groups with significantly different survival in the training dataset (median 2.20 vs. 8.93 years, log-rank test P < 0.001) and in the test dataset (median 2.68 vs. 4.24 years, log-rank test P = 0.004, n = 221, GSE14520). Further multivariate Cox regression analysis showed that the signature was an independent prognostic factor for patients with HCC. Compared with TNM stage and another reported three-gene model, the signature displayed improved survival prediction power in entire dataset (AUC signature = 0.66 vs. AUC TNM = 0.64 vs. AUC gene model = 0.60, n = 553). Stratification analysis shows that it can be used as an auxiliary marker for many traditional staging models.

Conclusions

We constructed an eight-gene signature that can be a novel prognostic marker to predict the survival of HCC patients.

UBE2C cell-free RNA in urine can discriminate between bladder cancer and hematuria

Background

There is growing interest in circulating nucleic acids as cancer detection biomarkers. Therefore, the aim of the present study was to identify a key urinary cell-free RNA marker that may assist in the diagnosis of BC.

Results

Five cell-free RNAs were selected as candidate cell-free RNAs from tissue microarray data. An area under the curve (AUC) cut-off value of 0.7 in receiver operating characteristic (ROC) curve analysis identified four urinary cell-free RNAs for further analysis (CDC20, ESM1, UBE2C, and CA9; AUC = 0.716, 0.704, 0.721, and 0.702, respectively). Binary logistic regression analysis revealed that high expression of UBE2C was significantly associated with BC (OR, 1.754; CI, 1.147–2.682; p = 0.010). Analysis of UBE2C expression in urine samples from BC patients and hematuria controls yielded an AUC of 0.839, with a sensitivity of 82.5% and a specificity of 76.2%. UBE2C levels was significantly increased in G2 and G3 tumors compared to normal controls (p <0.001, respectively).

Materials and Methods

Urine samples from 212 BC patients and 106 normal controls (64 healthy individuals and 42 with hematuria) were examined. The candidate cell-free RNAs identified from tissue microarrays derived from BC and normal control tissues was then measured in the urine samples.

Conclusions

The levels of urinary UBE2C cell-free RNA were significantly higher in BC samples than in normal and hematuria control samples. The higher levels of urinary UBE2C cell-free RNA in BC might reflect high expression in BC tissues. Therefore, urinary UBE2C cell-free RNA may be a valuable diagnostic marker for BC.

Urinary cell-free nucleic acid IQGAP3: a new non-invasive diagnostic marker for bladder cancer

Background

There is growing interest in developing new non-invasive diagnostic tools for bladder cancer (BC) that have better sensitivity and specificity than cystoscopy and cytology. This study examined the value of urinary cell-free nucleic acid (NA) as a diagnostic marker for BC.

Material and methods

A total of 81 patients (74 BC and 7 normal controls) were used for a tissue set, and 212 patients (92 BC and 120 normal controls) were used as a urine set. Expression of tissue mRNA and urinary cell-free NAs was then examined.

Results

Four candidate genes were top-ranked in the tissue microarray. Expression levels of two of these (IQGAP3 and TOP2A) in BC tissue and urine samples from BC patients were significantly higher than those in samples from the control groups. Binary logistic regression analysis of cell-free NA levels in urine samples revealed that IQGAP3 was significantly associated with BC: PicoGreen-adjusted odds ratio (OR), 3.434; confidence interval (CI), 2.999–4.180; P<0.001; RiboGreen-adjusted OR, 2.242; CI, 1.793–2.840; P<0.001. Further analysis of IQGAP3 urinary cell-free NAs with respect to tumor invasiveness and grade also yielded a high AUC, suggesting that IQGAP3 can discriminate between BC patients and non-cancer patients with hematuria.

Conclusions

Levels of IQGAP3 urinary cell-free NA in BC patients were significantly higher than those in normal controls or patients with hematuria. High levels of IQGAP3 urinary cell-free NA also reflected high expression in BC tissues. Therefore, IQGAP3 urinary cell-free NA may be a complementary diagnostic biomarker for BC.

Comparative Proteomics Analysis of Urine Reveals Down-Regulation of Acute Phase Response Signaling and LXR/RXR Activation Pathways in Prostate Cancer

Detecting prostate cancer (PCa) using non-invasive diagnostic markers still remains a challenge. The aim of this study was the identification of urine proteins that are sufficiently sensitive and specific to detect PCa in the early stages. Comparative proteomics profiling of urine from patients with PCa, benign prostate hyperplasia, bladder cancer, and renal cancer, coupled with bioinformatics analysis, were performed. Statistically significant difference in abundance showed 20 and 85 proteins in the 2-D DIGE/MS and label-free LC-MS/MS experiments, respectively. In silico analysis indicated activation, binding, and cell movement of subset of immune cells as the top affected cellular functions in PCa, together with the down-regulation of Acute Phase Response Signaling and Liver X Receptor/ Retinoid X Receptor (LXR/RXR) activation pathways. The most promising biomarkers were 35, altered in PCa when compared to more than one group. Half of these have confirmed localization in normal or PCa tissues. Twenty proteins (CD14, AHSG, ENO1, ANXA1, CLU, COL6A1, C3, FGA, FGG, HPX, PTGDS, S100A9, LMAN2, ITIH4, ACTA2, GRN, HBB, PEBP1, CTSB, SPP1) are oncogenes, tumor suppressors, and multifunctional proteins with highly confirmed involvement in PCa, while 9 (AZU1, IGHG1, RNASE2, PZP, REG1A, AMY1A, AMY2A, ACTG2, COL18A1) have been associated with different cancers, but not with PCa so far, and may represent novel findings. LC-MS/MS data are available via ProteomeXchange with identifier PXD008407.

Kinesin Family Member 11 mRNA Expression Predicts Prostate Cancer Aggressiveness

BACKGROUND

KIF11 (kinesin family member 11), a molecular motor protein, is essential to mitosis and cell cycle progression. Inhibitors of KIF11 have been developed as chemotherapeutic agents for the treatment of various cancers. Regarding prostate cancer (PCa), clinical trials using KIF11 inhibitors for the treatment of castration-resistant PCa have been initiated. We hypothesized that a relationship might exist between KIF11 expression and PCa. To investigate the functional activities and clinical usefulness of KIF11 in PCa, we used quantitative real-time reverse transcriptase polymerase chain reaction to monitor the KIF11 expression patterns.

MATERIALS AND METHODS

Tissue samples from 134 patients with PCa were analyzed using gene expression profiling and compared with tissues from 61 patients with benign prostatic hyperplasia. KIF11 expression was evaluated by real-time reverse transcriptase polymerase chain reaction and compared with indicators of tumor aggressiveness, such as prostate-specific antigen (PSA) levels, Gleason score (GS), and tumor stage (TNM stage).

RESULTS

KIF11 mRNA expression in tissue was significantly greater in PCa patients with elevated serum PSA levels (≥ 10 ng/mL), GS ≥ 8 [58(43.3%)], T stage ≥ T3, or metastatic disease than in those with PSA levels < 10 ng/mL, GS of 7, or T2 stage. Additionally, the expression was remarkably greater in patients with a GS of ≥ 9 than in patients with a GS of 3+4.

CONCLUSION

KIF11 expression might be indicative of PCa aggressiveness and could be useful as a prognostic marker for patients with PCa.

Value of urinary topoisomerase-IIA cell-free DNA for diagnosis of bladder cancer

PURPOSE

Topoisomerase-II alpha (TopoIIA ), a DNA gyrase isoform that plays an important role in the cell cycle, is present in normal tissues and various human cancers, and can show altered expression in both. The aim of the current study was to examine the value of urinary TopoIIA cell-free DNA as a noninvasive diagnosis of bladder cancer (BC).

MATERIALS AND METHODS

Two patient cohorts were examined. Cohort 1 (73 BC patients and seven controls) provided bladder tissue samples, whereas cohort 2 (83 BC patients, 54 nonmalignant hematuric patients, and 61 normal controls) provided urine samples. Real-time quantitative polymerase chain reaction was used to measure expression of TopoIIA mRNA in tissues and TopoIIA cell-free DNA in urine samples.

RESULTS

The results showed that expression of TopoIIA mRNA in BC tissues was significantly higher than that in noncancer control tissues (p<0.001). The expression of urinary TopoIIA cell-free DNA in BC patients was also significantly higher than that in noncancer patient controls and hematuria patients (p < 0.001 and p < 0.001, respectively). High expression of urinary TopoIIA cell-free DNA was also detected in muscle invasive bladder cancer (MIBC) when compared with nonmuscle invasive bladder cancer (NMIBC) (p=0.002). Receiver operating characteristics (ROC) curve analysis was performed to examine the sensitivity/specificity of urinary TopoIIA cell-free DNA for diagnosing BC, NMIBC, and MIBC. The areas under the ROC curve for BC, NMIBC, and MIBC were 0.741, 0.701, and 0.838, respectively.

CONCLUSIONS

In summary, the results of this study provide evidence that cell-free TopoIIA DNA may be a potential biomarker for BC.

Urinary Nucleic Acid TSPAN13-to-S100A9 Ratio as a Diagnostic Marker in Prostate Cancer

The potential use of urinary nucleic acids as diagnostic markers in prostate cancer (PCa) was evaluated. Ninety-five urine samples and 234 prostate tissue samples from patients with PCa and benign prostatic hyperplasia (BPH) were analyzed. Micro-array analysis was used to identify candidate genes, which were verified by the two-gene expression ratio and validated in tissue mRNA and urinary nucleic acid cohorts. Real-time quantitative polymerase chain reaction (qPCR) was used to measure urinary nucleic acid levels and tissue mRNA expression. The TSPAN13-to-S100A9 ratio was selected to determine the diagnostic value of urinary nucleic acids in PCa (P = 0.037) and shown to be significantly higher in PCa than in BPH in the mRNA and nucleic acid cohort analyses (P < 0.001 and P = 0.013, respectively). Receiver operating characteristic (ROC) analysis showed that the area under the ROC curve was 0.898 and 0.676 in tissue mRNA cohort and urinary nucleic acid cohort, respectively. The TSPAN13-to-S100A9 ratio showed a strong potential as a diagnostic marker for PCa. The present results suggest that the analysis of urine supernatant can be used as a simple diagnostic method for PCa that can be adapted to the clinical setting in the future.