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Molecule interacting with CasL 1 (MICAL1) is a crucial protein involved in cell motility, axon guidance, cytoskeletal dynamics, and gene transcription. This pan-cancer study analyzed MICAL1 across 33 cancer types using bioinformatics and experiments. Dysregulated expression, diagnostic potential, and prognostic value were assessed. Associations with tumor characteristics, immune factors, and drug sensitivity were explored. Enrichment analysis revealed MICAL1’s involvement in metastasis, angiogenesis, metabolism, and immune pathways. Functional experiments demonstrated its impact on renal carcinoma cells. These findings position MICAL1 as a potential biomarker and therapeutic target in specific cancers, warranting further investigation into its role in cancer pathogenesis.

ABSTRACTThis study aims to conduct a bibliometric analysis, employing visualization tools to examine literature pertaining to tumor immune evasion related to anti-CTLA-4 and anti-PD-1/PD-L1 therapy from 1999 to 2022. A special emphasis is placed on the interplay between tumor microenvironment, signaling pathways, immune cells and immune evasion, with data sourced from the Web of Science core collection (WoSCC). Advanced tools, including VOSviewer, Citespace, and Scimago Graphica, were utilized to analyze various parameters, such as co-authorship/co-citation patterns, regional contributions, journal preferences, keyword co-occurrences, and significant citation bursts. Out of 4778 publications reviewed, there was a marked increase in research focusing on immune evasion, with bladder cancer being notably prominent. Geographically, China, the USA, and Japan were the leading contributors. Prestigious institutions like MD Anderson Cancer Center, Harvard Medical School, Fudan University, and Sun Yat Sen University emerged as major players. Renowned journals in this domain included Frontiers in Immunology, Cancers, and Frontiers in Oncology. Ehen LP and Wang W were identified as prolific authors on this topic, while Topalian SL stood out as one of the most cited. Research current situation is notably pivoting toward challenges like immunotherapy resistance and the intricate signaling pathways driving drug resistance. This bibliometric study seeks to provide a comprehensive overview of past and current research trends, emphasizing the potential role of tumor microenvironment, signaling pathways and immune cells in the context of immune checkpoint inhibitors (ICIs) and tumor immune evasion.

Abstract. Background:. Dual-energy computed tomography (DECT) is purported to accurately distinguish uric acid stones from non-uric acid stones. However, whether DECT can accurately discriminate ammonium urate stones from uric acid stones remains unknown. Therefore, we aimed to explore whether they can be accurately identified by DECT and to develop a radiomics model to assist in distinguishing them. Methods:. This research included two steps. For the first purpose to evaluate the accuracy of DECT in the diagnosis of uric acid stones, 178 urolithiasis patients who underwent preoperative DECT between September 2016 and December 2019 were enrolled. For model construction, 93, 40, and 109 eligible urolithiasis patients treated between February 2013 and October 2022 were assigned to the training, internal validation, and external validation sets, respectively. Radiomics features were extracted from non-contrast CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a radiomics signature. Then, a radiomics model incorporating the radiomics signature and clinical predictors was constructed. The performance of the model (discrimination, calibration, and clinical usefulness) was evaluated. Results:. When patients with ammonium urate stones were included in the analysis, the accuracy of DECT in the diagnosis of uric acid stones was significantly decreased. Sixty-two percent of ammonium urate stones were mistakenly diagnosed as uric acid stones by DECT. A radiomics model incorporating the radiomics signature, urine pH value, and urine white blood cell count was constructed. The model achieved good calibration and discrimination {area under the receiver operating characteristic curve (AUC; 95% confidence interval [CI]), 0.944 (0.899–0.989)}, which was internally and externally validated with AUCs of 0.895 (95% CI, 0.796–0.995) and 0.870 (95% CI, 0.769–0.972), respectively. Decision curve analysis revealed the clinical usefulness of the model. Conclusions:. DECT cannot accurately differentiate ammonium urate stones from uric acid stones. Our proposed radiomics model can serve as a complementary diagnostic tool for distinguishing them in vivo.

Background The intricate task of diagnosing and managing small renal masses (SRMs) has become progressively convoluted within the realm of clinical practice. Contemporary clinical prediction instruments may succumb to a gradual decay in precision, coupled with an absence of unambiguous guidelines to navigate patient management. Methods This investigation was devised to formulate and authenticate nomograms for the overall survival (OS) and cancer- specific survival (CSS) among patients afflicted with SRMs. The study encompassed a cohort of 2558 pediatric patients diagnosed with SRMs over the period of 2000 to 2019. Independent prognostic indicators for OS and CSS, encompassing historical staging, chemotherapy regimens, surgical interventions, and pathological classifications, were ascertained through the employment of multivariate Cox proportional hazards regression analysis and backward stepwise selection. Results Through the utilization of multivariate Cox regression models, nomograms for OS and CSS were meticulously crafted, demonstrating commendable discrimination and calibration within the training set (OS C-index: 0.762, CSS C-index: 0.779). The validation set further corroborated the exemplary discrimination and calibration of the nomograms. Moreover, these nomograms adeptly differentiated between patient groups at elevated and diminished risk levels. Conclusion The nomograms delineated in this research provide propitious predictive accuracy for overall survival and cancer-specific survival in patients suffering from pediatric SRMs, thereby contributing to refined risk stratification and steering the optimal therapeutic course of action. The necessity for supplementary validation prevails before the translation of these findings into clinical practice.

BackgroundThe therapeutic effectiveness of immune checkpoint inhibitors (ICIs) in bladder cancer varies among individuals. Identifying reliable predictors of response to these therapies is crucial for optimizing patient outcomes.MethodsThis retrospective study analyzed 348 bladder cancer patients treated with ICIs, with additional validation using data from 248 patients at our institution who underwent PD-L1 immunohistochemical staining. We examined patient smoking history, clinicopathological characteristics, and immune phenotypes. The main focus was the correlation between smoking history and immunotherapy outcomes. Multivariate logistic and Cox proportional hazard regressions were used to adjust for confounders.ResultsThe study cohort comprised 348 bladder cancer patients receiving ICIs. Among them, 116 (33.3%) were never smokers, 197 (56.6%) were former smokers (median pack-years = 28), and 35 (10.1%) were current smokers (median pack-years = 40). Analysis revealed no statistically significant difference in overall survival across different smoking statuses (objective response rates were 11.4% for current smokers, 17.2% for never smokers, and 22.3% for former smokers; P = 0.142, 0.410, and 0.281, respectively). However, a notable trend indicated a potentially better response to immunotherapy in former smokers compared to current and never smokers. In the validation cohort of 248 patients from our institution, immunohistochemical analysis showed that PD-L1 expression was significantly higher in former smokers (55%) compared to current smokers (37%) and never smokers (47%). This observation underscores the potential influence of smoking history on the tumor microenvironment and its responsiveness to ICIs.ConclusionIn conclusion, our study demonstrates the importance of incorporating smoking history in predicting the response to immunotherapy in bladder cancer patients, highlighting its role in personalized cancer treatment approaches. Further research is suggested to explore the comprehensive impact of lifestyle factors on treatment outcomes.

Aims: To provide a comprehensive bibliometric overview of drug resistance in bladder cancer (BC) from 1999 to 2022, aiming to illuminate its historical progression and guide future investigative avenues. Methods: Literature on BC drug resistance between 1999 and 2022 was sourced from the Web of Science. Visual analyses were executed using Vosviewer and Citespace software, focusing on contributions by countries, institutions, journals, authors, references, and keywords. Results: From 2727 publications, a marked growth in BC drug resistance studies was discerned over the two decades. Prominent among all institutions is the University of Texas System. The majority of top-ranked journals were American. In authorship significance, McConkey DJ led in publications, while Bellmunt J dominated in citations. Research topics predominantly spanned cancer demographics, drug efficacy evaluations, molecular features, oncology subtypes, and individualized treatment strategies, with a notable contemporary emphasis on molecular mechanisms behind drug resistance and nuances of ICIs. Conclusions: Our bibliometric analysis charts the landscape of BC drug resistance research from 1999 to 2022. While the study of resistance mechanisms has been robust, there's an evident need for deeper exploration into the molecular intricacies and the potential of ICIs and targeted therapeutic strategies.

Abstract Background Non-muscle invasive bladder cancer (NMIBC) is known for its elevated recurrence rate, necessitating an enhancement in the current risk stratification for recurrence. The urine-based fluorescence in situ hybridization (FISH) assay has emerged as a noninvasive auxiliary tool for detecting bladder cancer. The aim of this study was to explore the potential relationship between the preoperative FISH assay and recurrence, and to develop a FISH-clinical nomogram for predicting the recurrence-free survival (RFS) in NMIBC patients. Methods In total, 332 eligible patients were enrolled from two hospitals. The SYSMH cohort was randomly assigned to the training set (n = 168) and the validation set I (n = 72) at a ratio of 7:3, while the SYSUTH cohort was allocated to the validation set II (n = 92). The correlation between the preoperative FISH assay and recurrence was determined through the Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used for model construction. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness. Results We uncovered that chromosome 7 aneuploidy, p16 locus loss, number of the positive FISH sites, and the FISH test result were significantly associated with tumor recurrence. Then, a FISH-clinical nomogram incorporating the FISH test result, T stage, associated CIS, tumor grade, and tumor status was developed. It showed favorable calibration and discrimination with a C-index of 0.683 (95%CI, 0.611–0.756) in the training set, which was confirmed in the validation set I and validation set II with C-indexes of 0.665 (95%CI, 0.565–0.765) and 0.778 (95%CI, 0.665–0.891), respectively. Decision curve analysis revealed the clinical usefulness of the nomogram. Moreover, our proposed nomogram significantly outperformed the guideline-recommended EORTC and CUETO scoring models. Conclusion Our study confirmed the prognostic value of the preoperative FISH assay and proposed a FISH-clinical nomogram to predict RFS in NMIBC patients. Our nomogram can serve as a more precise tool for recurrence risk stratification, which may optimize disease management in bladder cancer and improve patient prognosis.

Abstract Urothelial bladder cancer (UBC) is one of the most prevalent malignancies worldwide, with striking tumor heterogeneity. Elucidating the molecular mechanisms that can be exploited for the treatment of aggressive UBC is a particularly relevant goal. Protein ubiquitination is a critical post-translational modification (PTM) that mediates the degradation of target protein via the proteasome. However, the roles of aberrant protein ubiquitination in UBC development and the underlying mechanisms by which it drives tumor progression remain unclear. In this study, taking advantage of clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein (Cas) 9 technology, we identified the ubiquitin E3 ligase ANAPC11, a critical subunit of the anaphase-promoting complex/cyclosome (APC/C), as a potential oncogenic molecule in UBC cells. Our clinical analysis showed that elevated expression of ANAPC11 was significantly correlated with high T stage, positive lymph node (LN) metastasis, and poor outcomes in UBC patients. By employing a series of in vitro experiments, we demonstrated that ANAPC11 enhanced the proliferation and invasiveness of UBC cells, while knockout of ANAPC11 inhibited the growth and LN metastasis of UBC cells in vivo. By conducting immunoprecipitation coupled with mass spectrometry, we confirmed that ANAPC11 increased the ubiquitination level of the Forkhead transcription factor FOXO3. The resulting decrease in FOXO3 protein stability led to the downregulation of the cell cycle regulator p21 and decreased expression of GULP1, a downstream effector of androgen receptor signaling. Taken together, these findings indicated that ANAPC11 plays an oncogenic role in UBC by modulating FOXO3 protein degradation. The ANAPC11–FOXO3 regulatory axis might serve as a novel therapeutic target for UBC.

Background: The prognostic significance of tumor size with adrenocortical carcinoma (ACC) patients has not yet been thoroughly evaluated. Our objective was to investigate the influence of tumor size on prognostic value in adult ACC patients. Methods: The Surveillance, Epidemiology and End Results Program (SEER) was employed to identify adult ACC patients who had been diagnosed from 2004 to 2015. The “X-Tile” program determined the optimal cutoff value of tumor size. Cancer-specific survival (CSS) and overall survive (OS) were estimated. The survival outcomes and risk factors were analyzed by the Kaplan-Meier methods and the multivariable cox regression respectively. Results: A total 426 adult ACC patients were included. Univariable and multivariable cox analysis revealed age, larger tumor size and metastasis as consistent predictors of lower CSS and OS. The optimal cutoff value of tumor size was identified as 8.5 cm using X-tile software, and Kaplan-Meier method showed dramatic prognostic difference between patients with larger tumors (＞8.5 cm) and smaller tumors (≤8.5 cm) (log-rank test, P

BackgroundNeoadjuvant chemotherapy (NAC) followed by radical cystectomy (RC) remains the cornerstone of treatment for muscle-invasive bladder cancer (MIBC). While platinum-based regimens have demonstrated benefits in tumor downstaging and improved long-term survival for selected patients, they may pose risks for those who are ineligible or unresponsive to chemotherapy.ObjectiveWe undertook a bibliometric analysis to elucidate the breadth of literature on NAC in bladder cancer, discern research trajectories, and underscore emerging avenues of investigation.MethodsA systematic search of the Web of Science Core Collection (WoSCC) was conducted to identify articles pertaining to NAC in bladder cancer from 1999 to 2022. Advanced bibliometric tools, such as VOSviewer, CiteSpace, and SCImago Graphica, facilitated the examination and depicted the publication trends, geographic contributions, institutional affiliations, journal prominence, author collaborations, and salient keywords, emphasizing the top 25 citation bursts.ResultsOur analysis included 1836 publications spanning 1999 to 2022, indicating a growing trend in both annual publications and citations related to NAC in bladder cancer. The United States emerged as the predominant contributor in terms of publications, citations, and international collaborations. The University of Texas was the leading institution in publication output. “Urologic Oncology Seminars and Original Investigations” was the primary publishing journal, while “European Urology” boasted the highest impact factor. Shariat, Shahrokh F., and Grossman, H.B., were identified as the most prolific and co-cited authors, respectively. Keyword analysis revealed both frequency of occurrence and citation bursts, highlighting areas of concentrated study. Notably, the integration of immunochemotherapy is projected to experience substantial growth in forthcoming research.ConclusionsOur bibliometric assessment provides a panoramic view of the research milieu surrounding neoadjuvant chemotherapy for bladder cancer, encapsulating the present state, evolving trends, and potential future directions, with a particular emphasis on the promise of immunochemotherapy.

Abstract Background With the development of digital pathology and the renewal of deep learning algorithm, artificial intelligence (AI) is widely applied in tumor pathology. Previous researches have demonstrated that AI-based tumor pathology may help to solve the challenges faced by traditional pathology. This technology has attracted the attention of scholars in many fields and a large amount of articles have been published. This study mainly summarizes the knowledge structure of AI-based tumor pathology through bibliometric analysis, and discusses the potential research trends and foci. Methods Publications related to AI-based tumor pathology from 1999 to 2021 were selected from Web of Science Core Collection. VOSviewer and Citespace were mainly used to perform and visualize co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references and keywords in this field. Results A total of 2753 papers were included. The papers on AI-based tumor pathology research had been continuously increased since 1999. The United States made the largest contribution in this field, in terms of publications (1138, 41.34%), H-index (85) and total citations (35,539 times). We identified the most productive institution and author were Harvard Medical School and Madabhushi Anant, while Jemal Ahmedin was the most co-cited author. Scientific Reports was the most prominent journal and after analysis, Lecture Notes in Computer Science was the journal with highest total link strength. According to the result of references and keywords analysis, “breast cancer histopathology” “convolutional neural network” and “histopathological image” were identified as the major future research foci. Conclusions AI-based tumor pathology is in the stage of vigorous development and has a bright prospect. International transboundary cooperation among countries and institutions should be strengthened in the future. It is foreseeable that more research foci will be lied in the interpretability of deep learning-based model and the development of multi-modal fusion model.

Abstract Background Preoperative diagnosis of pheochromocytoma (PHEO) accurately impacts preoperative preparation and surgical outcome in PHEO patients. Highly reliable model to diagnose PHEO is lacking. We aimed to develop a magnetic resonance imaging (MRI)-based radiomic-clinical model to distinguish PHEO from adrenal lesions. Methods In total, 305 patients with 309 adrenal lesions were included and divided into different sets. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction, feature selection, and radiomics signature building. In addition, a nomogram incorporating the obtained radiomics signature and selected clinical predictors was developed by using multivariable logistic regression analysis. The performance of the radiomic-clinical model was assessed with respect to its discrimination, calibration, and clinical usefulness. Results Seven radiomics features were selected among the 1301 features obtained as they could differentiate PHEOs from other adrenal lesions in the training (area under the curve [AUC], 0.887), internal validation (AUC, 0.880), and external validation cohorts (AUC, 0.807). Predictors contained in the individualized prediction nomogram included the radiomics signature and symptom number (symptoms include headache, palpitation, and diaphoresis). The training set yielded an AUC of 0.893 for the nomogram, which was confirmed in the internal and external validation sets with AUCs of 0.906 and 0.844, respectively. Decision curve analyses indicated the nomogram was clinically useful. In addition, 25 patients with 25 lesions were recruited for prospective validation, which yielded an AUC of 0.917 for the nomogram. Conclusion We propose a radiomic-based nomogram incorporating clinically useful signatures as an easy-to-use, predictive and individualized tool for PHEO diagnosis.

BackgroundProstate cancer (PCa) is the second most common malignancy in men worldwide. Growing evidence substantiates the important role of immunotherapy in human tumors. Given that immunotherapy is often unsatisfactory on PCa, many studies have been conducted on PCa immunotherapy to improve treatment efficacy. However, no relevant bibliometric study of PCa immunotherapy has hitherto been reported. A bibliometric analysis was performed to evaluate the global scientific production of PCa immunotherapy research and characterize the development trends for future studies in this article.MethodsThe publications related to PCa immunotherapy were extracted from the Web of Science Core Collection. The contribution and co-occurrence relationships of countries/regions, institutions, journals, references, authors, and keywords were assessed and visualized by VOSviewer and CiteSpace to identify research hotspots and potential future trends.ResultsA total of 3,583 publications related to PCa immunotherapy from 1999 to 2021 were collected. The results of annual publications and citations exhibited a steady increase over the past 22 years. The National Cancer Institute in the USA published far more papers during the study than any institute. Accordingly, the USA had the most publications (n = 1,954, 54.54%). Gulley, James L. had the most number of published papers, and Small, Eric J. was the most co-cited authors in this field. Cancer Immunology Immunotherapy was the most productive journal, with 145 publications on PCa immunotherapy. Keyword cluster and keyword burst analyses showed that research in PCa immunotherapy shifted from “t cell infiltration” and “sipuleucel t” to “immune checkpoint inhibitor”, “CTLA-4”, and “PD-L1 expression”.ConclusionPCa immunotherapy has attracted much attention, reflected by the increasing number of annual publications and citations. Much emphasis has been placed on exploring the complex immunogenicity and tumor microenvironment for PCa and identifying the patient population who can benefit from immunotherapy. Combining immune checkpoint inhibitors with other therapeutic options and cancer vaccines represents the future development trends in PCa immunotherapy.

PurposeImmune checkpoint blockade agents were shown to provide a survival advantage in urothelial carcinoma, while some patients got minimal benefit or side effects. Therefore, we aimed to investigate the prognostic value of m6A methylation regulators, and developed a nomogram for predicting the response to atezolizumab in urothelial carcinoma patients.MethodsA total of 298 advanced urothelial carcinoma patients with response data in the IMvigor210 cohort were included. Differential expressions of 23 m6A methylation regulators in different treatment outcomes were conducted. Subsequently, a gene signature was developed in the training set using the least absolute shrinkage and selection operator (LASSO) regression. Based on the multivariable logistic regression, a nomogram was constructed by incorporating the gene signature and independent clinicopathological predictors. The performance of the nomogram was assessed by its discrimination, calibration, and clinical utility with internal validation.ResultsSix m6A methylation regulators, including IGF2BP1, IGF2BP3, YTHDF2, HNRNPA2B1, FMR1, and FTO, were significantly differentially expressed between the responders and non-responders. These six regulators were also significantly correlated with the treatment outcomes. Based on the LASSO regression analysis, the gene signature consisting of two selected m6A methylation regulators (FMR1 and HNRNPA2B1) was constructed and showed favorable discrimination. The nomogram integrating the gene signature, TMB, and PD-L1 expression on immune cells, showed favorable calibration and discrimination in the training set (AUC 0.768), which was confirmed in the validation set (AUC 0.755). Decision curve analysis confirmed the potential clinical usefulness of the nomogram.ConclusionsThis study confirmed the prognostic value of FMR1 and HNRNPA2B1, and constructed a nomogram for individualized prediction of the response to atezolizumab in patients with urothelial carcinoma, which may aid in making treatment strategies.

BackgroundRenal cell carcinoma (RCC) is one of the most lethal urological malignancies, and because early-stage RCC is asymptomatic, many patients present metastatic diseases at first diagnosis. With the development of immunotherapy, the treatment of RCC has entered a new stage and has made a series of progress. This study mainly outlines the knowledge map and detects the potential research hotspots by using bibliometric analysis.MethodsPublications concerning RCC immunotherapy from 2002 to 2021 in the Web of Science Core Collection were collected. Visualization and statistical analysis were mainly performed by freeware tools VOSviewer, CiteSpace, R software, and Microsoft Office Excel 2019.ResultsA total of 3,432 papers were collected in this study, and the annual number of papers and citations showed a steady growth trend. The United States is the leading country with the most high-quality publications and is also the country with the most international cooperation. The University of Texas MD Anderson Cancer Center is the most productive organization. The Journal of Clinical Oncology is the highest co-cited journal, and Brian I. Rini is both the most prolific author and the author with the largest centrality. The current research hotspots may be focused on “immune checkpoint inhibitors (ICIs),” “PD-1,” and “mammalian target of rapamycin.”ConclusionImmunotherapy has a bright future in the field of RCC treatment, among which ICIs are one of the most important research hotspots. The main future research directions of ICI-based immunotherapy may focus on combination therapy, ICI monotherapy, and the development of new predictive biomarkers.

BackgroundWith the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including prostate cancer. Facts have proved that AI has broad prospects in the accurate diagnosis and treatment of prostate cancer.ObjectiveThis study mainly summarizes the research on the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores possible future research hotspots.MethodsThe articles and reviews regarding application of AI in prostate cancer between 1999 and 2020 were selected from Web of Science Core Collection on August 23, 2021. Microsoft Excel 2019 and GraphPad Prism 8 were applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 5.8.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field.ResultsA total of 2,749 articles were selected in this study. AI-related research on prostate cancer increased exponentially in recent years, of which the USA was the most productive country with 1,342 publications, and had close cooperation with many countries. The most productive institution and researcher were the Henry Ford Health System and Tewari. However, the cooperation among most institutions or researchers was not close even if the high research outputs. The result of keyword analysis could divide all studies into three clusters: “Diagnosis and Prediction AI-related study”, “Non-surgery AI-related study”, and “Surgery AI-related study”. Meanwhile, the current research hotspots were “deep learning” and “multiparametric MRI”.ConclusionsArtificial intelligence has broad application prospects in prostate cancer, and a growing number of scholars are devoted to AI-related research on prostate cancer. Meanwhile, the cooperation among various countries and institutions needs to be strengthened in the future. It can be projected that noninvasive diagnosis and accurate minimally invasive treatment through deep learning technology will still be the research focus in the next few years.

BackgroundSome studies have revealed a close relationship between metabolism-related genes and the prognosis of bladder cancer. However, the relationship between metabolism-related long non-coding RNAs (lncRNA) regulating the expression of genetic material and bladder cancer is still blank. From this, we developed and validated a prognostic model based on metabolism-associated lncRNA to analyze the prognosis of bladder cancer.MethodsGene expression, lncRNA sequencing data, and related clinical information were extracted from The Cancer Genome Atlas (TCGA). And we downloaded metabolism-related gene sets from the human metabolism database. Differential expression analysis is used to screen differentially expressed metabolism-related genes and lncRNAs between tumors and paracancer tissues. We then obtained metabolism-related lncRNAs associated with prognosis by correlational analyses, univariate Cox analysis, and logistic least absolute shrinkage and selection operator (LASSO) regression. A risk scoring model is constructed based on the regression coefficient corresponding to lncRNA calculated by multivariate Cox analysis. According to the median risk score, patients were divided into a high-risk group and a low-risk group. Then, we developed and evaluated a nomogram including risk scores and Clinical baseline data to predict the prognosis. Furthermore, we performed gene-set enrichment analysis (GSEA) to explore the role of these metabolism-related lncRNAs in the prognosis of bladder cancer.ResultsBy analyzing the extracted data, our research screened out 12 metabolism-related lncRNAs. There are significant differences in survival between high and low-risk groups divided by the median risk scoring model, and the low-risk group has a more favorable prognosis than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk score was closely related to the prognosis of bladder cancer. Then we established a nomogram based on multivariate analysis. After evaluation, the modified model has good predictive efficiency and clinical application value. Furthermore, the GSEA showed that these lncRNAs affected bladder cancer prognosis through multiple links.ConclusionsA predictive model was established and validated based on 12 metabolism-related lncRNAs and clinical information, and we found these lncRNA affected bladder cancer prognosis through multiple links.

BackgroundA survival benefit was observed in metastatic bladder cancer patients who underwent primary tumor resection, but it was still confusing which patients are suitable for the surgery. For this purpose, we developed a model to screen stage M1 patients who would benefit from primary tumor resection.MethodsPatients with metastatic bladder cancer were screened from the Surveillance, Epidemiology, and End Results database (2004–2016) and then were divided into surgery (partial or complete cystectomy) group and non-surgery group. To balance the characteristics between them, a 1:1 propensity score matching analysis was applied. A hypothesis was proposed that the received primary tumor resection group has a more optimistic prognosis than the other group. The multivariable Cox model was used to explore the independent factors of survival time in two groups (beneficial and non-beneficial groups). Logistic regression was used to build a nomogram based on the significant predictive factors. Finally, a variety of methods are used to evaluate our model.ResultsA total of 7,965 patients with metastatic bladder cancer were included. And 3,314 patients met filtering standards, of which 545 (16.4%) received partial or complete cystectomy. Plots of the Kaplan–Meier and subgroup analyses confirmed our hypothesis. After propensity score matching analysis, a survival benefit was still observed that the surgery group has a longer median overall survival time (11.0 vs. 6.0 months, p < 0.001). Among the surgery cohort, 303 (65.8%) patients lived longer than 6 months (beneficial group). Differentiated characteristics included age, gender, TNM stage, histologic type, differentiation grade, and therapy, which were integrated as predictors to build a nomogram. The nomogram showed good discrimination in both training and validation cohorts (area under the receiver operating characteristic curve (AUC): 0.806 and 0.742, respectively), and the calibration curves demonstrated good consistency. Decision curve analysis showed that the nomogram was clinically useful. Compared with TNM staging, our model shows a better predictive value in identifying optimal patients for primary tumor resection.ConclusionsA practical predictive model was created and verified, which might be used to identify the optimal candidates for the partial or complete cystectomy group of the primary tumor among metastatic bladder cancer.

Background: Alterations in the brain cortical structures of patients with chronic kidney disease (CKD) have been reported; however, the cause has not been determined yet. Herein, we used Mendelian randomization (MR) to reveal the causal effect of kidney damage on brain cortical structure. Methods: Genome-wide association studies summary data of estimated glomerular filtration rate (eGFR) in 480,698 participants from the CKDGen Consortium were used to identify genetically predicted eGFR. Data from 567,460 individuals from the CKDGen Consortium were used to assess genetically determined CKD; 302,687 participants from the UK Biobank were used to evaluate genetically predicted albuminuria. Further, data from 51,665 patients from the ENIGMA Consortium were used to assess the relationship between genetic predisposition and reduced eGFR, CKD, and progressive albuminuria with alterations in cortical thickness (TH) or surficial area (SA) of the brain. Magnetic resonance imaging was used to measure the SA and TH globally and in 34 functional regions. Inverse-variance weighted was used as the primary estimate whereas MR Pleiotropy RESidual Sum and Outlier, MR-Egger and weighted median were used to detect heterogeneity and pleiotropy. Findings: At the global level, albuminuria decreased TH (β = −0.07 mm, 95% CI: −0.12 mm to −0.02 mm, P = 0.004); at the functional level, albuminuria reduced TH of pars opercularis gyrus without global weighted (β = −0.11 mm, 95% CI: −0.16 mm to −0.07 mm, P = 3.74×10−6). No pleiotropy was detected. Interpretation: Kidney damage causally influences the cortex structure which suggests the existence of a kidney-brain axis. Funding: This study was supported by the Science and Technology Planning Project of Guangdong Province (Grant No. 2020A1515111119 and 2017B020227007), the National Key Research and Development Program of China (Grant No. 2018YFA0902803), the National Natural Science Foundation of China (Grant No. 81825016, 81961128027, 81772719, 81772728), the Key Areas Research and Development Program of Guangdong (Grant No. 2018B010109006), Guangdong Special Support Program (2017TX04R246), Grant KLB09001 from the Key Laboratory of Malignant Tumor Gene Regulation and Target Therapy of Guangdong Higher Education Institutes, and Grants from the Guangdong Science and Technology Department (2020B1212060018).

Abstract Background Clinical outcome of adrenocortical carcinoma (ACC) varies because of its heterogeneous nature and reliable prognostic prediction model for adult ACC patients is limited. The objective of this study was to develop and externally validate a nomogram for overall survival (OS) prediction in adult patients with ACC after surgery. Methods Based on the data from the Surveillance Epidemiology, and End Results (SEER) database, adults patients diagnosed with ACC between January 1988 and December 2015 were identified and classified into a training set, comprised of 404 patients diagnosed between January 2007 and December 2015, and an internal validation set, comprised of 318 patients diagnosed between January 1988 and December 2006. The endpoint of this study was OS. The nomogram was developed using a multivariate Cox proportional hazards regression algorithm in the training set and its performance was evaluated in terms of its discriminative ability, calibration, and clinical usefulness. The nomogram was then validated using the internal SEER validation, also externally validated using the Cancer Genome Atlas set (TCGA, 82 patients diagnosed between 1998 and 2012) and a Chinese multicenter cohort dataset (82 patients diagnosed between December 2002 and May 2018), respectively. Results Age at diagnosis, T stage, N stage, and M stage were identified as independent predictors for OS. A nomogram incorporating these four predictors was constructed using the training set and demonstrated good calibration and discrimination (C-index 95% confidence interval [CI], 0.715 [0.679–0.751]), which was validated in the internal validation set (C-index [95% CI], 0.672 [0.637–0.707]), the TCGA set (C-index [95% CI], 0.810 [0.732–0.888]) and the Chinese multicenter set (C-index [95% CI], 0.726 [0.633–0.819]), respectively. Encouragingly, the nomogram was able to successfully distinguished patients with a high-risk of mortality in all enrolled patients and in the subgroup analyses. Decision curve analysis indicated that the nomogram was clinically useful and applicable. Conclusions The study presents a nomogram that incorporates clinicopathological predictors, which can accurately predict the OS of adult ACC patients after surgery. This model and the corresponding risk classification system have the potential to guide therapy decisions after surgery.

Background: Metastasis is a major obstacle in the treatment of bladder cancer (BC). Circular RNAs exert various functions in the aggressive biological behaviour of cancers. In this study, we aimed to elucidate how circPICALM influences BC metastasis. Methods: The expression of circPICALM was analysed by real-time PCR. The tumourigenic properties of BC cells were evaluated using in vitro migration, invasion, and wound healing assays and an in vivo footpad model. The interaction between circPICALM and miR-1265 was confirmed by pull-down and dual-luciferase reporter assays and biotin-labelled miRNA capture. The interaction of STEAP4 and focal adhesion kinase (FAK) was confirmed by co-immunoprecipitation. Findings: CircPICALM was downregulated in BC tissues, and low circPICALM expression was related to advanced T stage, high grade, lymph node positivity and poor overall survival. Overexpression of circPICALM inhibited the metastasis of BC cells, and DHX9 negatively regulated circPICALM levels. CircPICALM colocalized with miR-1265 and acted as a sponge for this miRNA, and the pro-invasion effect of miR-1265 was abolished by circPICALM overexpression. STEAP4, a target of miR-1265, suppressed metastasis; it bound to FAK to prevent autophosphorylation at Y397 and influenced EMT in BC cells. Interpretation: CircPICALM can inhibit BC metastasis and bind to miR-1265 to block its pro-invasion activity. STEAP4 is a target of miR-1265 and is related to FAK phosphorylation and EMT. Fund: This research was supported by National Natural Science Foundation of China, No.81772728, National Natural Science Foundation of China, No.81772719, National Natural Science Foundation of China No.81572514. Keywords: circPICALM, miR-1265, STEAP4, EMT, Metastasis, Bladder cancer

Accumulating evidence show that Poly C Binding Protein 1 (PCBP1) is deleted in distinct types of tumors as a novel tumor suppressor, but its tumor suppression mechanism remains elusive. Here, we firstly describe that downregulation of PCBP1 is significantly associated with clinical ovarian tumor progression. Mechanistically, PCBP1 overexpression affects various autophagy-related genes expression at various expression levels to attenuate the intrinsic cell autophagy, including the autophagy-initiating ULK, ATG12, ATG7 as well as the bona fide marker of autophagosome, LC3B. Accordingly, knockdown of the endogenous PCBP1 in turn enhances autophagy and less cell death. Meanwhile, PCBP1 upregulates p62/SQSTM1 via inhibition p62/SQSTM1 autophagolysome and proteasome degradation as well as its mRNA stability, consequently accompanying with the caspase 3 or 8 activation for tumor cell apoptosis. Importantly, clinical ovary cancer sample analysis consistently validates the relevance of PCBP1 expression to both p62/SQSTM1 and caspase-8 to overall survival, and indicates PCBP1 may be a master player to repress tumor initiation. Taken together, our results uncover the tumorigenic mechanism of PCBP1 depletion and suggest that inhibition of tumor cell autophagy with autophagic inhibitors could be an effective therapeutical strategy for PCBP1-deficient tumor.

Abstract Background We aimed to investigate the roles of hemoglobin (Hb) concentrations and dynamic change during treatment on outcomes of patients with extremity osteosarcoma. Methods We retrospectively analysed 133 patients with Enneking stage IIB extremity osteosarcoma who underwent standard treatments, including univariate and multivariate analyses of patient charateritics, Hb concentrations and changes during pretreatment, neoadjuvant, adjuvant chemotherapy, and decreased Hb levels (ΔHb) to assess their prognostic value in 5-year overall survival (OS) and lung metastasis-free survival (LMFS). Results Five-year OS or LMFS were similar between patients who were anaemic and non-anaemic during pretreatment, neoadjuvant or adjuvant chemotherapy. Patients with continuously decreasing Hb had lower 5-year OS (52.3%) than those without continuous Hb decrease (68.5%, P = 0.04). Patients with ΔHb > 7.6 g/L had lower 5-year OS (57.5%) than those with ΔHb ≤7.6 g/L (75.8%, P = 0.04). However, continuous Hb decrease had no prognostic effect on 5-year LMFS. Subgroup analyses showed that patients who were anaemic during pretreatment, neoadjuvant, or adjuvant chemotherapy with ΔHb ≤7.6 g/L had better outcomes than those with ΔHb > 7.6 g/L (P 7.6 predicted poor5-year OS in patients with Enneking stage IIB extremity osteosarcoma. Attempts to correct anaemia and their effects on outcomes for osteosarcoma patients should be investigated in future trials.

Summary: Cancer stem cells (CSCs) are responsible for tumor initiation, chemoresistance, metastasis, and relapse, but the underlying molecular origin of CSCs remains elusive. Here we identified that metastatic phosphatase of regenerating liver 3 (PRL-3) transcriptionally upregulates SOX2 in the expansion of CSC sub-population from normal cancer cells. Mechanistically, SOX2 upregulation is attributed to the binding of the acetylated myocyte enhancer factor 2A (MEF2A) to SOX2 promoter in tumor cells. In parallel, PRL-3 competitively binds to Class IIa histone deacetylase 4 (HDAC4) to facilitate HDAC4 translocation, leading to the disassociation of HDAC4 from MEF2A and histones. The released MEF2A and histones thus remain acetylated and render the subsequent accessibility of the acetylated MEF2A to SOX2 promoter region. Clinical relevance among PRL-3, SOX2, and HDAC4 is validated in ovary cancer samples. Therefore, this PRL-3-HDAC4-MEF2A/histones-SOX2 signaling axis would be a potential therapeutic target in inhibiting ovarian cancer metastasis and relapse. : Biological Sciences; Molecular Biology; Stem Cells Research; Cancer Subject Areas: Biological Sciences, Molecular Biology, Stem Cells Research, Cancer