Single-Cell Genomics-Based Molecular Algorithm for Early Cancer Detection.

As one of the prime applications of liquid biopsy, the detection of tumor-derived whole cells and molecular markers is enabled in a noninvasive means before symptoms or hints from imaging procedures used for cancer screening. However, liquid biopsy is not a diagnostic test of malignant diseases per se because it fails to establish a definitive cancer diagnosis. Although single-cell genomics provides a genome-wide genetic alternation landscape, it is technologically challenging to confirm cell malignancy of a suspicious cell in body fluids due to unknown technical noise of single-cell sequencing and genomic variation among cancer cells, especially when tumor tissues are unavailable for sequencing as the reference. To address this challenge, we report a molecular algorithm, named scCancerDx, for confirming cell malignancy based on single-cell copy number alternation profiles of suspicious cells from body fluids, leading to a definitive cancer diagnosis. The scCancerDx algorithm has been trained with normal cells and cancer cell lines and validated with single tumor cells disassociated from clinical samples. The established scCancerDx algorithm then validates hexokinase 2 (HK2) as an efficient metabolic function-associated marker of identifying disseminated tumor cells in different body fluids across many cancer types. The HK2-based test, together with scCancerDx, has been investigated for the early detection of bladder cancer (BC) at a preclinical phase by detecting high glycolytic HK2high tumor cells in urine. Early BC detection improves patient prognosis and avoids radical resection for enhancing life quality.

Clinical significance of serum high sensitive C-reactive protein/albumin ratio in primary prostate biopsy.

Objective: The purpose of this study was to investigate the clinical significance of serum high sensitive C-reactive protein/albumin ratio in primary prostate biopsy. Methods: Retrospective analysis was done on the clinical data of 1679 patients who had their first transrectal or perineal prostate biopsy at our situation from 2010 to 2018. Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) were the pathologic diagnoses in 819 and 860 cases, respectively. A comparison was made between the HAR differences between PCa and BPH patients as well as the positive prostate biopsy rate differences between groups with increased and normal HAR. The results of the prostate biopsy were examined using logistic regression, and a model for predicting prostate cancer was created. The receiver characteristic curve (ROC) was used to determine the model's prediction effectiveness. The clinical models integrated into HAR were evaluated for their potential to increase classification efficacy using net reclassification improvement (NRI) and integrated discrimination improvement (IDI). According to the Gleason score (GS) categorization system, prostate cancer patients were separated into low, middle, and high GS groups. The differences in HAR between the various groups were then compared. The prevalence of high GSPCa and metastatic PCa in normal populations and the prevalence of higher HAR in prostate cancer patients were compared using the chi-square test. Result: Patients with PCa had a median HAR (upper quartile to lower quartile) of 0.0379 (10-3), patients with BPH had a median HAR (0.0137 (10-3)), and the difference was statistically significant (p<0.05). Patients with increased HAR and the normal group, respectively, had positive prostate biopsy rates of 52% (435/839)and 46% (384/840), and the difference was statistically significant (p<0.05). Logistic regression analysis showed that HAR (OR=3.391, 95%CI 2.082 ~ 4.977, P < 0.05), PSA density (PSAD) (OR=7.248, 95%CI 5.005 ~ 10.495, P < 0.05) and age (OR=1.076, 95%CI 1.056 ~ 1.096, P < 0.05) was an independent predictor of prostate biopsy results. Two prediction models are built: a clinical model based on age and PSAD, and a prediction model that adds HAR to the clinical model. The two models' ROC had area under the curves (AUC) of 0.814 (95%CI 0.78-0.83) and 0.815 (95%CI 0.79-0.84), respectively. When compared to a single blood total PSA (tPSA) with an AUC of 0.746 (95%CI 0.718-0.774), they were all superior. Nevertheless, there was no statistically significant difference (p<0.05) between the two models. We assessed the prediction model integrated into HAR's capacity to increase classification efficiency using NRI and IDI, and we discovered that NRI>0, IDI>0, and the difference was statistically significant (P>0.05).There was a statistically significant difference in HAR between various GS groups for individuals who had prostate cancer as a consequence of biopsy (p<0.05). The incidence of high GS and metastatic patients was statistically significantly greater (p<0.05) in the HAR elevated group (90.1%and 39.3%, respectively) than in the HAR normal group (84.4% and 12.0%). Conclusion: Prostate biopsy results that were positive were impacted by HAR, an independent factor that increased with the rate of PCa discovery. Patients with elevated HAR had a greater risk of high GS as well as metastatic PCa among those with recently diagnosed prostate cancer through prostate biopsy.

Texture analysis based on PI-RADS 4/5-scored magnetic resonance images combined with machine learning to distinguish benign lesions from prostate cancer.

Background: The global morbidity and mortality of prostate cancer (PCa) increase sharply every year. Early diagnosis is essential; it determines survival and outcome. So, this study extracted the texture features of apparent diffusion coefficient images in multiparametric magnetic resonance imaging (mp-MRI) and built machine learning models based on radiomics texture analysis (TA) to determine its ability to distinguish benign from PCa lesions using the Prostate Imaging Reporting and Data System (PI-RADS) 4/5 score. Methods: We enrolled 103 patients who underwent mp-MRI examinations and transrectal ultrasound and magnetic resonance fusion imaging (TRUS-MRI) targeted prostate biopsy and obtained pathological confirmation at our hospital from August 2017 to January 2020. We used ImageJ software to obtain texture feature parameters based on apparent diffusion coefficient (ADC) images, then standardized texture feature parameters, and used LASSO regression to reduce multiple feature parameters; 70% of the cases were randomly selected from the PCa group and the benign prostate hyperplasia group as the training set. The remaining 30% was used as the test set. The machine learning classification model for identifying benign and malignant prostate lesions was constructed using the feature parameters after dimensionality reduction. The clinical indicators were statistically analyzed, and we constructed a machine learning classification model based on clinical indicators of benign and malignant prostate lesions. Finally, we compared the model's performance based on radiomics texture features and clinical indicators to identify benign and malignant prostate lesions in PI-RADS 4/5 score. Results: The area under the curve (AUC) of the R-logistic model test set was 0.838, higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-logistic classification model formula is as follow: Y_radiomics=9.396-7.464*median ADC-0.584*kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the corresponding C-logistic classification model formula is as follows: Y_clinical =-2.608+0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set was 0.860, smaller than the training set R-logistic classification model AUC value of 0.936. Conclusions: Radiomics combined with the machine learning classifier model has strong classification performance in identifying benign and PCa in PI-RADS 4/5 score. Various treatments and outcomes for PCa patients can be applied clinically.

Laparoscopic radical resection for large-volume renal carcinoma: via retroperitoneal or transperitoneal approach?

PURPOSE: The aim of this study was to investigate the clinical efficacy and safety of laparoscopic radical resection through retroperitoneal and transperitoneal approaches in treating large-volume renal carcinoma. METHODS: A total of 116 patients with large-volume (>7 cm) renal carcinoma underwent laparoscopic radical resection for renal carcinoma. Among them, 58 were treated through retroperitoneal approach (Retroperitoneal group), and 58 were treated through transperitoneal approach (Abdominal group). The levels of interleukin-6 (IL-6), IL-12 and IL-1ß in the patients were compared before and after operation. Furthermore, the levels of tumor markers were explored, and the tumor recurrence and survival of the patients were followed up and recorded. RESULTS: Compared with those in Abdominal group, the patients in Retroperitoneal group had remarkably shorter operation time, time of renal artery occlusion, time of intestinal exhaust and length of hospital stay after operation as well as notably smaller intraoperative blood loss. The levels of IL-6, IL-12 and IL-1ß were elevated after operation in both groups in comparison with those before operation. Besides, the concentrations of serum CA50, CA125 and CEA declined obviously after treatment in the two groups in contrast with those before treatment, while no statistically significant differences in the concentrations of serum CA50, CA125 and CEA were observed between the two groups after treatment. The follow-up results indicated that the average survival and progression-free survival were 18.3 months and 16.0 months, respectively, in Retroperitoneal group, and 19.1 months and 16.8 months, respectively, in Abdominal group. CONCLUSIONS: The retroperitoneal laparoscopic radical resection for large-volume renal carcinoma possesses exact therapeutic effects, and it has shorter operation time, less blood loss, fewer impacts on inflammatory responses in patients and higher safety than transperitoneal laparoscopic radical resection.

Comparison of Retzius-Sparing Robot-Assisted Radical Prostatectomy vs. Conventional Robot-Assisted Radical Prostatectomy: An Up-to-Date Meta-Analysis.

Background: The Retzius space-sparing robot-assisted radical prostatectomy (RS-RARP) has shown better results in urinary continence, but its efficacy and safety compared to conventional robot-assisted radical prostatectomy (c-RARP) remain controversial. Material and Methods: A research was conducted in Medline via PubMed, Cochrane Library, EMBASE, and Web of Science up to January 4, 2021, to identify studies comparing RS-RARP to c-RARP. We used RevMan 5.3 and STATA 14.0 for meta-analysis. Results: A total of 14 studies involving 3,129 participants were included. Meta-analysis showed no significant difference in positive surgical margins (PSMs), but the RS-RARP group had significantly higher PSM rates in the anterior site [odds ratio (OR) = 2.25, 95% CI: 1.22-4.16, P = 0.01]. Postoperative continence in RS-RARP group at 1 month (OR = 5.72, 95% CI: 3.56-9.19, P < 0.01), 3 months (OR = 6.44, 95% CI: 4.50-9.22, P < 0.01), 6 months (OR = 8.68, 95% CI: 4.01-18.82, P < 0.01), and 12 months (OR = 2.37, 95% CI: 1.20-4.70, P = 0.01) was significantly better than that in the c-RARP group. In addition, the RS-RARP group had a shorter console time (mean difference = -16.28, 95% CI: -27.04 to -5.53, P = 0.003) and a lower incidence of hernia (OR = 0.35, 95% CI: 0.19-0.67, P = 0.001). However, there were no significant differences in estimated blood loss, pelvic lymph node dissection rate, postoperative complications, 1-year-biochemical recurrence rate, and postoperative sexual function. Conclusions: Compared with c-RARP, RS-RARP showed better recovery of continence, shorter console time, and lower incidence of hernia. Although there was no significant difference in overall PSM, we suggest that the surgeon should be more careful if the lesion is in the anterior prostate.

Development of a highly sensitive digital PCR assay to quantify long non-coding RNA MYU in urine samples which exhibited great potential as an alternative diagnostic biomarker for prostate cancer.

BACKGROUND: The diagnostic methods of prostate cancer (PCa) present major drawbacks in that serum prostate specific antigen (PSA) testing lacks specificity for PCa and prostate needle biopsy is a painful and highly invasive procedure for patients. Thus, new alternative screening methods which are specific and non-invasive both in the early detection and in the clinical definitive diagnosis of PCa are in urgent need. Long non-coding RNA MYU has been shown to promote PCa cell proliferation and migration, and is significantly upregulated both at the cellular and tumor tissue level. Therefore, long non-coding RNA MYU may be a new potential diagnostic biomarker for PCa. METHODS: In the present study, we successfully developed a highly sensitive digital PCR assay to detect long non-coding RNA in clinical urine samples. dPCR was carried out using Qx200 ddPCR EvaGreen Supermix (Bio-Rad) according to the manufacturer's instructions. RESULTS: Our results indicated that the digital PCR assay showed better linearity, repeatability, and reproducibility when compared with real-time quantitative PCR. In addition, we identified the normalized MYU level and used the digital PCR assay to measure it in 100 clinical urine samples. Our study showed that the normalized MYU level is a promising diagnostic biomarker for predicting and evaluating the malignancy of PCa. CONCLUSIONS: Our findings presented a non-invasive liquid biopsy method to detect an alternative diagnostic parameter which can assist the diagnosis of PCa in clinical practice.

Clinical association between pre-treatment levels of plasma fibrinogen and bone metastatic burden in newly diagnosed prostate cancer patients.

BACKGROUND: Due to the different treatments for low-volume metastatic prostate cancer (PCa) as well as high-volume ones, evaluation of bone metastatic status is clinically significant. In this study, we evaluated the correlation between pre-treatment plasma fibrinogen and the burden of bone metastasis in newly diagnosed PCa patients. METHODS: A single-center retrospective analysis, focusing on prostate biopsies of newly diagnosed PCa patients, was performed. A total of 261 patients were enrolled in this study in a 4-year period. All subjects were submitted to single-photon emission computerized tomography-computed tomography to confirm the status of bone metastasis and, if present, the number of metastatic lesions would then be calculated. Clinical information such as age, prostate-specific antigen (PSA), fibrinogen, clinical T stage, and Gleason score were collected. Patients were divided into three groups: (i) a non-metastatic group, (ii) a high volume disease (HVD) group (>3 metastases with at least one lesion outside the spine), and (iii) a low volume disease (LVD) group (metastatic patients excluding HVD ones). The main statistical methods included non-parametric Mann-Whitney test, Spearman correlation, receiver operating characteristic (ROC) curves, and logistic regression. RESULTS: Fibrinogen positively correlated with Gleason score (r = 0.180, P = 0.003), PSA levels (r = 0.216, P < 0.001), and number of metastatic lesions (r = 0.296, P < 0.001). Compared with the non-metastatic and LVD groups, the HVD group showed the highest PSA (104.98 ng/mL, median) and fibrinogen levels (3.39 g/L, median), as well as the largest proportion of Gleason score >7 (86.8%). Both univariate (odds ratio [OR] = 2.16, 95% confidential interval [CI]: 1.536-3.038, P < 0.001) and multivariate (OR = 1.726, 95% CI: 1.206-2.472, P = 0.003) logistic regressions showed that fibrinogen was independently associated with HVD. The ROC curve suggested that fibrinogen acts as a predictor of HVD patients, yielding a cut-off of 3.08 g/L, with a sensitivity of 0.684 and a specificity of 0.760 (area under the curve = 0.739, 95% CI: 0.644-0.833, P < 0.001). CONCLUSIONS: Pre-treatment plasma fibrinogen is positively associated with bone metastatic burden in PCa patients. Our results indicate that fibrinogen might be a potential predictor of HVD.

PSCA rs1045531 Polymorphism and the Risk of Prostate Cancer in a Chinese Population Undergoing Prostate Biopsy.

BACKGROUND AND PURPOSE: This study explored the association between a single-nucleotide polymorphism of prostate stem cell antigen and prostate cancer in Chinese patients undergoing prostate biopsy. MATERIALS AND METHODS: DNA from 416 patients undergoing prostate biopsy was typed for the prostate stem cell antigen rs1045531 single-nucleotide polymorphism. The frequency of the rs1045531 polymorphism in patients with prostate cancer and in patients with benign prostatic hyperplasia was compared. Associations between the polymorphism and the risk of prostate cancer, prostate special antigen, Gleason score, and clinical stage were analyzed. RESULTS: Statistically significant differences in the distribution of the rs1045531 genotypes and alleles were found between prostate cancer and benign prostatic hyperplasia in patients undergoing prostate biopsy ( P = .035 and .046, respectively). We found that the rs1045531 AC genotype was significantly associated with a high risk of prostate cancer in the heterozygote model (AC vs CC; odds ratio = 2.383, 95% confidence interval: 1.198-4.741, χ2 = 6.229, P = .013) and the dominant model (AA/AC vs CC; odds ratio = 2.169, 95% confidence interval: 1.112-4.229, χ2 = 5.228, P = .022). However, susceptibility of prostate cancer was decreased in the homozygote model (AA vs CC; odds ratio = 0.828, 95% confidence interval: 0.143-4.805, P = .601). When considering clinical factors, the rs1045531 showed an association with prostate special antigen of 10 ng/mL or greater, a Gleason score of 7 or greater, and a size of T2 or greater. CONCLUSION: Men with the rs1045531 AC genotype of prostate stem cell antigen were at higher risk of prostate cancer in Chinese patients undergoing prostate biopsy.

E-cadherin gene promoter hypermethylation may contribute to the risk of bladder cancer among Asian populations.

There are increasing scientific evidences suggesting that E-cadherin gene promoter hypermethylation may contribute to the development and progression of bladder cancer, but existing studies have yielded inconclusive results. This meta-analysis aims to assess the role of E-cadherin promoter hypermethylation in bladder carcinogenesis. We conducted an extensive literature search for relevant studies on PubMed, Embase, Web of Science, Cochrane Library, and CBM databases from their inception through May 1st, 2013. This meta-analysis was performed using the STATA 12.0 software. Crude risk ratio (RR) with 95% confidence interval (CI) was calculated. Ten clinical studies were included in this meta-analysis with a total of 620 bladder cancer samples,199 normal adjacent samples and 131 normal urothelium tissue. Our meta-analysis revealed that the methylation frequencies in bladder cancer tissues were obviously higher than those in normal control tissues (RR = 2.02, 95%CI: 1.00­4.12, P = 0.050). Subgroup analysis by ethnicity indicated that higher methylation frequencies were observed in bladder cancer tissues among Asian populations (RR = 2.35, 95%CI: 1.11­4.95, P = 0.025), but not among Caucasian populations (RR = 1.62, 95%CI: 0.48­5.53, P = 0.439). Univariate and multivariate meta-regression analyses showed that ethnicity may be the major source of heterogeneity (Pb0.05).No publication bias was detected in this meta-analysis (P=0.358). The present meta-analysis indicates that E-cadherin gene promoter hypermethylation may contribute to increased risk of bladder cancer among Asian populations.

Resistive index of prostatic capsular arteries as a predictor of prostate cancer in patients undergoing initial prostate biopsy.

To evaluate the value of resistive index (RI) of prostatic capsular arteries in diagnosis and evaluation of prostate cancer (PCa) in Chinese patients undergoing initial prostate biopsy. A total of 532 consecutive patients undergoing prostate biopsy were enrolled in this study. RI was measured on the largest transverse section of prostate for each individual. The predictive value of RI was evaluated using multivariate logistic regression and receiver operating characteristic (ROC) curve analyses. PCa was identified in 217 (40.79%) patients. RI was 0.69 ± 0.08 and 0.8 ± 0.08 for patients without and with PCa (p < 0.01). On logistic regression RI was significantly associated with PCa (p < 0.01). Using ROC analysis RI outperformed tPSA in prediction of PCa in all patients [area under ROC curve (AUC) = 0.83, 0.78, respectively]. With the cutoff value of 0.73, RI discriminated PCa from non-PCa patients with 81.9% sensitivity, 75.9% specificity and 77.63% diagnostic accuracy. Furthermore, The AUC for RI in the discrimination of PCa from non-PCa patients in a subset with PSA of 4 to 10 ng/ml, high grade from non-high grade PCa patients and advanced from localized PCa patients was 0.70, 0.77 and 0.80, higher than other parameters (p < 0.05). RI is proved a practicable parameter in identifying patients at risk for PCa and predicting the grade and stage of PCa before initial prostate biopsy. The value of RI should be further explored in the future.