Background: To compare the safety and efficacy of retrograde intrarenal surgery (RIRS) and modified Ultra-mini percutaneous nephrolithotomy (UMP) in semi-supine combined lithotomy position for the management of 1.5-3.5 cm lower pole renal stones (LPSs). Methods: A total of 63 patients with 1.5-3.5 cm LPSs who underwent RIRS (n = 33) or modified UMP (n = 30) in diameter between January 2017 and January 2019 were analyzed retrospectively. Modified UMP was performed in semi-supine combined lithotomy position and a 9.5/11.5 F ureteral access sheath (UAS) was inserted during the procedure in order to maintain low pelvic pressure and to facilitate the removal of stone fragments. Base-line parameters, stone characteristics, illness condition, operation time, postoperative hemoglobin (Hb) drop, postoperative creatinine (Cr) elevation, length of hospital stay, length of postoperative hospital stay, stone-free rate (SFR) and complications were compared between the two groups. Results: There were no significant differences between the two groups in base-line parameters, stone characteristics and illness condition. The mean operating time of RIRS group was longer than UMP group (95.61 ± 21.9 vs. 55.0 ± 16.1 min, p < 0.001). The mean postoperative Hb drop was less in RIRS group (7.42 ± 4.7 vs. 15.70 ± 9.8 g/L, p < 0.001). The length of hospital stay and postoperative hospital stay for RIRS were shorter than UMP (4.76 ± 1.1 vs. 5.83 ± 0.8 d, p < 0.001, 2.97 ± 0.9 vs. 4.07 ± 0.9 d, p < 0.001). The Early SFR was higher in UMP group (54.5 vs. 80.0%, p < 0.050) while SFR at 1-month and 3-months postoperatively was similar in both groups (p = 0.504, p = 0.675). There were no significant differences between the two groups in complications (p = 0.228). Conclusion: For patients with 1.5-3.5 cm LPSs, both modified UMP and RIRS are safe and viable. The modified UMP technique was used in this study, application semi-supine combined lithotomy position and the retention of UAS can improve the surgical efficiency and maintain low pressure perfusion in the kidney, which resulted in superior treatment efficacy. Therefore, we highly recommend this technique for LPSs with heavy stone burdens.

The process of chemical carcinogenesis is a complex multistage process initiated by DNA damage in growth control genes. Carcinogens enter the body from a variety of sources, but most require metabolic activation before they can damage DNA. There are multiple protective processes that include detoxification and conjugation, DNA repair and programmed cell death. Most of these functions exhibit wide interindividual variation in the population and thus are thought to affect cancer risk. The role of gene-environment interactions is being explored, and current data indicate that genetic susceptibilities can modify carcinogen exposures from the diet and tobacco smoking, although much more data exist for the latter. This review addresses the relationships of human carcinogenesis to these interindividual differences of phase I, phase II and DNA repair enzymes.

BackgroundProstate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa.MethodsDifferential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the “edgeR” package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells.ResultsFive glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. In vitro experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown.ConclusionThe risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR.

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BackgroundBoth lncRNAs and glycolysis are considered to be key influencing factors in the progression of bladder cancer (BCa). Studies have shown that glycolysis-related lncRNAs are an important factor affecting the overall survival and prognosis of patients with bladder cancer. In this study, a prognostic model of BCa patients was constructed based on glycolysis-related lncRNAs to provide a point of reference for clinical diagnosis and treatment decisions.MethodsThe transcriptome, clinical data, and glycolysis-related pathway gene sets of BCa patients were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Set Enrichment Analysis (GSEA) official website. Next, differentially expressed glycolysis-related lncRNAs were screened out, glycolysis-related lncRNAs with prognostic significance were identified through LASSO regression analysis, and a risk scoring model was constructed through multivariate Cox regression analysis. Then, based on the median of the risk scores, all BCa patients were divided into either a high-risk or low-risk group. Kaplan-Meier (KM) survival analysis and the receiver operating characteristic (ROC) curve were used to evaluate the predictive power of the model. A nomogram prognostic model was then constructed based on clinical indicators and risk scores. A calibration chart, clinical decision curve, and ROC curve analysis were used to evaluate the predictive performance of the model, and the risk score of the prognostic model was verified using the TCGA data set. Finally, Gene Set Enrichment Analysis (GSEA) was performed on glycolysis-related lncRNAs.ResultsA total of 59 differentially expressed glycolysis-related lncRNAs were obtained from 411 bladder tumor tissues and 19 pericarcinomatous tissues, and 9 of those glycolysis-related lncRNAs (AC099850.3, AL589843.1, MAFG-DT, AC011503.2, NR2F1-AS1, AC078778.1, ZNF667-AS1, MNX1-AS1, and AC105942.1) were found to have prognostic significance. A signature was then constructed for predicting survival in BCa based on those 9 glycolysis-related lncRNAs. ROC curve analysis and a nomogram verified the accuracy of the signature.ConclusionThrough this study, a novel prognostic prediction model for BCa was established based on 9 glycolysis-related lncRNAs that could effectively distinguish high-risk and low-risk BCa patients, and also provide a new point of reference for clinicians to make individualized treatment and review plans for patients with different levels of risk.