Ensemble Machine Learning Model Incorporating Radiomics and Body Composition for Predicting Intraoperative HDI in PPGL.

CONTEXT: Intraoperative hemodynamic instability (HDI) can lead to cardiovascular and cerebrovascular complications during surgery for pheochromocytoma/paraganglioma (PPGL). OBJECTIVES: We aimed to assess the risk of intraoperative HDI in patients with PPGL to improve surgical outcome. METHODS: A total of 199 consecutive patients with PPGL confirmed by surgical pathology were retrospectively included in this study. This cohort was separated into 2 groups according to intraoperative systolic blood pressure, the HDI group (n = 101) and the hemodynamic stability (HDS) group (n = 98). It was also divided into 2 subcohorts for predictive modeling: the training cohort (n = 140) and the validation cohort (n = 59). Prediction models were developed with both the ensemble machine learning method (EL model) and the multivariate logistic regression model using body composition parameters on computed tomography, tumor radiomics, and clinical data. The efficiency of the models was evaluated with discrimination, calibration, and decision curves. RESULTS: The EL model showed good discrimination between the HDI group and HDS group, with an area under the curve of (AUC) of 96.2% (95% CI, 93.5%-99.0%) in the training cohort, and an AUC of 93.7% (95% CI, 88.0%-99.4%) in the validation cohort. The AUC values from the EL model were significantly higher than the logistic regression model, which had an AUC of 74.4% (95% CI, 66.1%-82.6%) in the training cohort and an AUC of 74.2% (95% CI, 61.1%-87.3%) in the validation cohort. Favorable calibration performance and clinical applicability of the EL model were observed. CONCLUSION: The EL model combining preoperative computed tomography-based body composition, tumor radiomics, and clinical data could potentially help predict intraoperative HDI in patients with PPGL.

CT-based assessment of sarcopenia for differentiating wild-type from mutant-type gastrointestinal stromal tumor.

Non-invasive prediction for KIT/PDGFRA status in GIST is a challenging problem. This study aims to evaluate whether CT based sarcopenia could differentiate KIT/PDGFRA wild-type gastrointestinal stromal tumor (wt-GIST) from the mutant-type GIST (mu-GIST), and to evaluate genetic features of GIST. A total of 174 patients with GIST (wt-GIST = 52) were retrospectively identified between January 2011 to October 2019. A sarcopenia nomogram was constructed by multivariate logistic regression. The performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. Genomic data was obtained from our own specimens and also from the open databases cBioPortal. Data was analyzed by R version 3.6.1 and clusterProfiler ( http://cbioportal.org/msk-impact ). There were significantly higher incidence (75.0% vs. 48.4%) and more severe sarcopenia in patients with wt-GIST than in patients with mu-GIST. Multivariate logistic regression analysis showed that sarcopenia score (fitted based on age, gender and skeletal muscle index), and muscle fat index were independent predictors for higher risk of wt-GIST (P < 0.05 for both the training and validation cohorts). Our sarcopenia nomogram achieved a promising efficiency with an AUC of 0.879 for the training cohort, and 0.9099 for the validation cohort with a satisfying consistency in the calibration curve. Favorable clinical usefulness was observed using decision curve analysis. The additional gene sequencing analysis based on both our data and the external data demonstrated aberrant signal pathways being closely associated with sarcopenia in the wt-GIST. Our study supported the use of CT-based assessment of sarcopenia in differentiating the wt-GIST from the mu-GIST preoperatively.

CT radiomics identifying non-responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer.

BACKGROUND AND PURPOSE: Early detection of non-response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT. MATERIALS AND METHODS: Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non-response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application. RESULTS: This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non-responders and 101 responders) and 64 patients in the validation cohort (21 non-responders and 43 responders). For predicting non-response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance. CONCLUSION: Pretreatment CT radiomics achieved satisfying performance in predicting non-response to nCRT and could be helpful to assist in treatment planning for patients with LARC.

Neoadjuvant immunotherapy, chemotherapy, and combination therapy in muscle-invasive bladder cancer: A multi-center real-world retrospective study.

To parallelly compare the efficacy of neoadjuvant immunotherapy (tislelizumab), neoadjuvant chemotherapy (gemcitabine and cisplatin), and neoadjuvant combination therapy (tislelizumab + GC) in patients with muscle-invasive bladder cancer (MIBC) and explore the efficacy predictors, we perform a multi-center, real-world cohort study that enrolls 253 patients treated with neoadjuvant treatments (combination therapy: 98, chemotherapy: 107, and immunotherapy: 48) from 15 tertiary hospitals. We demonstrate that neoadjuvant combination therapy achieves the highest complete response rate and pathological downstaging rate compared with neoadjuvant immunotherapy or chemotherapy. We develop and validate an efficacy prediction model consisting of pretreatment clinical characteristics, which can pinpoint candidates to receive neoadjuvant combination therapy. We also preliminarily reveal that patients who achieve pathological complete response after neoadjuvant treatments plus maximal transurethral resection of the bladder tumor may be safe to receive bladder preservation therapy. Overall, this study highlights the benefit of neoadjuvant combination therapy based on tislelizumab for MIBC.

CX3CL1-induced CD16+ monocytes extravasation in myeloperoxidase-ANCA-associated vasculitis correlates with renal damage.

Background: Monocytes are involved in the pathogenesis of ANCA-associated vasculitis (AAV). Monocyte/macrophages are the dominant infiltrating cells in the glomeruli of patients with myeloperoxidase-AAV (MPO-AAV). However, how human monocyte subsets extravasate to the kidney in MPO-AAV with renal damage is unclear. Methods: 30 MPO-AAV patients with renal damage and 22 healthy controls were enrolled in this study. Monocyte subsets and monocyte-related chemokines in the blood and kidneys of MPO-AAV patients were detected. The chemoattractant activity of the CX3CL1-CX3CR1 axis on CD16+ monocytes was observed. The effect of MPO-ANCA on the migration of CD16+ monocytes to human glomerular endothelial cells (HGECs) was detected by flow cytometry and transwell migration assay. Results: Compared with controls, CD16+ monocytes were significantly decreased in the blood and increased in the glomeruli of MPO-AAV patients with renal damage. The level of CX3CL1, but not CCL2, was significantly increased in the plasma of MPO-AAV patients. CX3CL1 co-localized with glomerular endothelial cells in MPO-AAV patients with renal damage. Moreover, we initially found that MPO-ANCA promotes an increase of the chemokine CX3CL1 on HGECs, imposing recruitment on CD16+ monocytes. Finally, the percentage of CD16+ monocytes in the blood was found to be positively correlated with estimated glomerular filtration rate (eGFR) and negatively correlated with urinary protein creatinine ratio in MPO-AAV patients with renal damage. Furthermore, the urinary protein creatinine ratio was positively correlated with the infiltrating of CD14+ and CD16+ cells in the kidneys. Conclusion: Enhanced extravasation of CD16+ monocytes to the kidney via the CX3CL1-CX3CR1 axis may be involved in renal damage in MPO-AAV.

THEM6: A Novel Molecular Biomarker Predicts Tumor Microenvironment, Molecular Subtype, and Prognosis in Bladder Cancer.

Background: Thioesterase superfamily member 6 (THEM6) has been implicated in the development and progression of various cancers. However, prior research emphasized on its regulatory role merely, we aim to investigate the effect of THEM6 gene on the immunological role and its relationship with molecular subtype in bladder cancer (BLCA). Methods: Through pan-cancer analysis, we explored the THEM6 expression pattern and immunological role using The Cancer Genome Atlas (TCGA) database. In addition, we performed a correlation of THEM6 and its immunological functions, including immunomodulators, immune checkpoints, cancer immunity cycles, T cell inflamed score, and tumor-infiltrating immune cells in the BLCA tumor microenvironment (TME) based on TCGA and BLCA microarray cohort from Xiangya Hospital. We also assessed the accuracy of THEM6 in predicting the molecular subtype and its response to different interventions in BLCA. Finally, we computed and validated a prediction model established by THEM6-related different expressed immune-related genes that might help in BLCA prognosis. Results: THEM6 led to immunosuppression in BLCA TME. Furthermore, there was a downregulation in the immunological functions. Besides, THEM6 could effectively distinguish BLCA molecular subtypes, and THEM6 low expression implied basal subtype that was more effective to several interventions, such as immune checkpoint blockade (ICB) therapies, neoadjuvant chemotherapy, and radiotherapy. While THEM6 high expression indicated luminal subtype, hyperprogression and better response to targeted therapies, such as blocking THEM6 and several immune-inhibited oncogenic pathways. Conclusions: THEM6 may be with potential immune-modulating properties and may become a potential new immunotherapy target for BLCA. THEM6 could accurately predict the molecular subtype of BLCA, which was helpful for guiding the treatment. Simultaneously, the prediction model may exhibit an excellent predictive value in patients with BLCA.

Pre-Treatment Computed Tomography Radiomics for Predicting the Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Retrospective Study.

Background and Purpose: Computerized tomography (CT) scans are commonly performed to assist in diagnosis and treatment of locally advanced rectal cancer (LARC). This study assessed the usefulness of pretreatment CT-based radiomics for predicting pathological complete response (pCR) of LARC to neoadjuvant chemoradiotherapy (nCRT). Materials and Methods: Patients with LARC who underwent nCRT followed by total mesorectal excision surgery from July 2010 to December 2018 were enrolled in this retrospective study. A total of 340 radiomic features were extracted from pretreatment contrast-enhanced CT images. The most relevant features to pCR were selected using the least absolute shrinkage and selection operator (LASSO) method and a radiomic signature was generated. Predictive models were built with radiomic features and clinico-pathological variables. Model performance was assessed with decision curve analysis and was validated in an independent cohort. Results: The pCR was achieved in 44 of the 216 consecutive patients (20.4%) in this study. The model with the best performance used both radiomics and clinical variables including radiomic signatures, distance to anal verge, lymphocyte-to-monocyte ratio, and carcinoembryonic antigen. This combined model discriminated between patients with and without pCR with an area under the curve of 0.926 and 0.872 in the training and the validation cohorts, respectively. The combined model also showed better performance than models built with radiomic or clinical variables alone. Conclusion: Our combined predictive model was robust in differentiating patients with and without response to nCRT.

CT imaging findings of renal epithelioid lipid-poor angiomyolipoma.

OBJECTIVES: To identify specific imaging and clinicopathological features of a rare potentially malignant epithelioid variant of renal lipid-poor angiomyolipoma (E-lpAML). METHODS: A total of 20 patients with E-lpAML and 43 patients with other lpAML were retrospectively included. Multiphase computed tomography (CT) imaging features and clinicopathological findings were recorded. Independent predictors for E-lpAML were identified using multivariate logistic regression and were used to construct a diagnostic score for differentiation of E-lpAML from other lpAML. RESULTS: The E-lpAML group consisted of 6 men and 14 women (age median ± SD: 39.45 ± 15.70, range: 16.0-68.0 years). E-lpAML tended to appear as hyperdense mass lesions located at the renal sinus (n = 8, 40%) or at the renal cortex (n = 12, 60%), with a "fast-in and slow-out" enhancement pattern (n = 20, 100%), cystic degeneration (n = 18, 90%), "eyeball" sign (n = 11, 55%), and tumor neo-vasculature (n = 15, 75%) on CT. Multivariate logistic regression analysis showed that the independent predictors for diagnosing E-lpAML were cystic degeneration on CT imaging and CT value of the tumor in corticomedullary phase of enhancement. A predictive model was built with the two predictors, achieving an area under the curve (AUC) of 93.5% (95% confidence interval (95%CI): 84.3-98.2%) with a sensitivity of 95.0% (95%CI: 75.1-99.9%) and a specificity of 83.72% (95%CI: 69.3-93.2%). CONCLUSION: We identified specific CT imaging features and predictors that could contribute to the correct diagnosis of E-lpAML. Our findings should be helpful for clinical management of E-lpAML which could potentially be malignant and may require nephron-sparing surgery while other lpAML tumors which are benign require no intervention. KEY POINTS: • It is important to differentiate renal epithelioid lipid-poor angiomyolipoma (E-lpAML) from other lpAML because of differences in clinical management. • E-lpAML tumors tend to be large hyperdense tumors in the renal sinus with cystic degeneration and "fast-in and slow-out" pattern of enhancement. • Our CT imaging-based predictive model was robust in its performance for predicting E-lpAML from other lpAML tumors.

Pre-treatment CT-based radiomics nomogram for predicting microsatellite instability status in colorectal cancer.

OBJECTIVES: Stratification of microsatellite instability (MSI) status in patients with colorectal cancer (CRC) improves clinical decision-making for cancer treatment. The present study aimed to develop a radiomics nomogram to predict the pre-treatment MSI status in patients with CRC. METHODS: A total of 762 patients with CRC confirmed by surgical pathology and MSI status determined with polymerase chain reaction (PCR) method were retrospectively recruited between January 2013 and May 2019. Radiomics features were extracted from routine pre-treatment abdominal pelvic computed tomography (CT) scans acquired as part of the patients' clinical care. A radiomics nomogram was constructed using multivariate logistic regression. The performance of the nomogram was evaluated using discrimination, calibration, and decision curves. RESULTS: The radiomics nomogram incorporating radiomics signatures, tumor location, patient age, high-density lipoprotein expression, and platelet counts showed good discrimination between patients with non-MSI-H and MSI-H, with an area under the curve (AUC) of 0.74 [95% CI, 0.68-0.80] in the training cohort and 0.77 [95% CI, 0.68-0.85] in the validation cohort. Favorable clinical application was observed using decision curve analysis. The addition of pathological characteristics to the nomogram failed to show incremental prognostic value. CONCLUSIONS: We developed a radiomics nomogram incorporating radiomics signatures and clinical indicators, which could potentially be used to facilitate the individualized prediction of MSI status in patients with CRC. KEY POINTS: • There is an unmet need to non-invasively determine MSI status prior to treatment. However, the traditional radiological evaluation of CT is limited for evaluating MSI status. • Our non-invasive CT imaging-based radiomics method could efficiently distinguish patients with high MSI disease from those with low MSI disease. • Our radiomics approach demonstrated promising diagnostic efficiency for MSI status, similar to the commonly used IHC method.

Preoperative Magnetic Resonance Imaging Radiomics for Predicting Early Recurrence of Glioblastoma.

PURPOSE: Early recurrence of glioblastoma after standard treatment makes patient care challenging. This study aimed to assess preoperative magnetic resonance imaging (MRI) radiomics for predicting early recurrence of glioblastoma. PATIENTS AND METHODS: A total of 122 patients (training cohort: n = 86; validation cohort: n = 36) with pathologically confirmed glioblastoma were included in this retrospective study. Preoperative brain MRI images were analyzed for both radiomics and the Visually Accessible Rembrandt Image (VASARI) features of glioblastoma. Models incorporating MRI radiomics, the VASARI parameters, and clinical variables were developed and presented in a nomogram. Performance was assessed based on calibration, discrimination, and clinical usefulness. RESULTS: The nomogram consisting of the radiomic signatures, the VASARI parameters, and blood urea nitrogen (BUN) values showed good discrimination between the patients with early recurrence and those with later recurrence, with an area under the curve of 0.85 (95% CI, 0.77-0.94) in the training cohort and 0.84 [95% CI, 0.71-0.97] in the validation cohort. Decision curve analysis demonstrated favorable clinical application of the nomogram. CONCLUSION: This study showed the potential usefulness of preoperative brain MRI radiomics in predicting the early recurrence of glioblastoma, which should be helpful in personalized management of glioblastoma.

CT-Based Sarcopenic Nomogram for Predicting Progressive Disease in Advanced Non-Small-Cell Lung Cancer.

BACKGROUND: It is prudent to identify the risk for progressive disease (PD) in patients with non-small-cell lung cancer (NSCLC) who undergo platinum-based chemotherapy. The present study aimed to develop a CT imaging-based sarcopenic nomogram for predicting the risk of PD prior to chemotherapy treatment. METHODS: We retrospectively enrolled patients with NSCLC who underwent platinum-based chemotherapy. Imaging-based body composition parameters such as skeletal muscle index (SMI) for assessment of sarcopenia were obtained from pre-chemotherapy chest CT images at the level of the eleventh thoracic vertebral body (T11). Sarcopenic nomogram was constructed using multivariate logistic regression and performance of the nomogram was evaluated by discrimination, calibration curve, and decision curve. RESULTS: Sixty (14.7%) of the 408 patients in the study cohort developed PD during chemotherapy. The prediction nomogram for developing PD achieved a moderate efficiency with an area under the curve (AUC) of 0.75 (95% CI: 0.69-0.80) for the training cohort, and 0.76 (95%CI: 0.68-0.84) for the validation cohort, as well as a good performance of consistence (bootstrap for training cohort: 0.75 ± 0.02; validation cohort: 0.74 ± 0.06). Favorable clinical application was observed in the decision curve analysis. CONCLUSION: Our CT-based sarcopenic nomogram showed the potential for an individualized prediction of progression for patients with NSCLC receiving platinum-based chemotherapy.

N6-Methyladenosine in Cancer Immunotherapy: An Undervalued Therapeutic Target.

N6-methylation of adenosine (m6A), a post-transcriptional regulatory mechanism, is the most abundant nucleotide modification in almost all types of RNAs. The biological function of m6A in regulating the expression of oncogenes or tumor suppressor genes has been widely investigated in various cancers. However, recent studies have addressed a new role of m6A modification in the anti-tumor immune response. By modulating the fate of targeted RNA, m6A affects tumor-associated immune cell activation and infiltration in the tumor microenvironment (TME). In addition, m6A-targeting is found to affect the efficacy of classical immunotherapy, which makes m6A a potential target for immunotherapy. Although m6A modification together with its regulators may play the exact opposite role in different tumor types, targeting m6A regulators has been shown to have wide implications in several cancers. In this review, we discussed the link between m6A modification and tumor with an emphasis on the importance of m6A in anti-tumor immune response and immunotherapy.

Lamin-A interacting protein Hsp90 is required for DNA damage repair and chemoresistance of ovarian cancer cells.

Ovarian cancer is the most malignant gynecologic cancer. Previous studies found that lamin-A was associated with DNA damage repair proteins but the underlying mechanism remains unclear. We speculate that this may be related to its interacting proteins, such as Hsp90. The aim of this study is to investigate the effects of Hsp90 on DNA damage repair and chemoresistance of ovarian cancer cells. In our research, co-immunoprecipitation (co-IP) and mass spectrometry (MS) were used to identify proteins interacting with lamin-A and the interaction domain. Next, the relationship between lamin-A and Hsp90 was explored by Western blotting (WB) and immunofluorescence staining. Then, effect of Hsp90 inhibition on DNA damage repair was assessed through detecting Rad50 and Ku80 by WB. Furthermore, to test the roles of 17-AAG on cell chemosensitivity, CCK-8 and colony formation assay were carried out. Meanwhile, IC50 of cells were calculated, followed by immunofluorescence to detect DNA damage. At last, the mouse xenograft model was used in determining the capacity of 17-AAG and DDP to suppress tumor growth and metastatic potential. The results showed that lamin-A could interact with Hsp90 via the domain of lamin-A1-430. Besides, the distribution of Hsp90 could be affected by lamin-A. After lamin-A knockdown, Hsp90 decreased in the cytoplasm and increased in the nucleus, suggesting that the interaction between lamin-A and Hsp90 may be related to the nucleocytoplasmic transport of Hsp90. Moreover, inhibition of Hsp90 led to an obvious decrease in the expression of DSBs (DNA double-strand break) repair proteins, as well as cell proliferation ability upon DDP treatment and IC50 of DDP, causing more serious DNA damage. In addition, the combination of 17-AAG and DDP restrained the growth of ovarian cancer efficiently in vivo and prolonged the survival time of tumor-bearing mice.

Trace the profile and function of circular RNAs in Sertoli cell only syndrome.

Studies increasingly show the involvement of circular RNAs (circRNAs) in several diseases. This study aims to explore the circRNA expression pattern in the testicular tissues of patients with Sertoli only cell syndrome (SCOS) and their potential functions. High throughput circRNA microarray analysis indicated that 399 circRNAs were upregulated and 1195 were down-regulated (fold change >2, P < 0.05) in SCOS relative to obstructive azoospermia (OA). The hsa_circRNA_101222, hsa_circRNA_001387, hsa_circRNA_001153, hsa_circRNA_101373 and hsa_circRNA_103864 were validated by qRT-PCR. Furthermore, the hosting genes of the differentially expressed circRNAs (DEcircRNAs) were enriched in biological processes related to cell cycle and intercellular communication. Also, the overlapping genes between the hosting genes of SCOS-related DEcircRNAs and those highly expressed in Sertoli cells of non-obstructive azoospermia (NOA) were enriched in immune cell development and cell communication. Taken together, aberrantly expressed circRNAs likely mediate SCOS development by regulating the function of Sertoli cells and the spermatogenic microenvironment.

Siglec15 shapes a non-inflamed tumor microenvironment and predicts the molecular subtype in bladder cancer.

Rationale: Siglec15 is an emerging target for normalization cancer immunotherapy. However, pan-cancer anti-Siglec15 treatment is not yet validated and the potential role of Siglec15 in bladder cancer (BLCA) remains elusive. Methods: We comprehensively evaluated the expression pattern and immunological role of Siglec15 using pan-cancer analysis based on RNA sequencing data obtained from The Cancer Genome Atlas. We then systematically correlated Siglec15 with immunological characteristics in the BLCA tumor microenvironment (TME), including immunomodulators, cancer immunity cycles, tumor-infiltrating immune cells (TIICs), immune checkpoints, and T cell inflamed score. We also analyzed the role of Siglec15 in predicting the molecular subtype and the response to several treatment options in BLCA. Our results were validated in several public cohorts as well as our BLCA tumor microarray cohort, the Xiangya cohort. We developed an immune risk score (IRS), validated it, and tested its ability to predict the prognosis and response to cancer immunotherapy. Results: We found that Siglec15 was specifically overexpressed in the TME of various cancers. We hypothesize that Siglec15 designs a non-inflamed TME in BLCA based on the evidence that Siglec15 negatively correlated with immunomodulators, TIICs, cancer immunity cycles, immune checkpoints, and T cell inflamed score. Bladder cancer with high Siglec15 expression was not sensitive to cancer immunotherapy, but exhibited a higher incidence of hyperprogression. High Siglec15 levels indicated a luminal subtype of BLCA characterized by lower immune infiltration, lower response to cancer immunotherapy and neoadjuvant chemotherapy, but higher response to anti-angiogenic therapy and targeted therapies such as blocking Siglec15, ß-catenin, PPAR-γ, and FGFR3 pathways. Notably, a combination of anti-Siglec15 and cancer immunotherapy may be a more effective strategy than monotherapy. IRS can accurately predict the prognosis and response to cancer immunotherapy. Conclusions: Anti-Siglec15 immunotherapy might be suitable for BLCA treatment as Siglec15 correlates with a non-inflamed TME in BLCA. Siglec15 could also predict the molecular subtype and the response to several treatment options.

Development and validation of a predictive nomogram for the risk of recurrence in patients with cystitis glandularis.

BACKGROUND: Most patients with cystitis glandularis (CG) suffer from recurrence after primary treatment. Therefore, we performed this multicenter study to clarify the recurrent risk factors and constructed a predictive nomogram for the risk of recurrence. Also, we try to investigate the correlation between CG and bladder cancer. METHODS: Consecutive patients with pathologically confirmed CG were divided into training and validation sets. Clinicopathological characters were collected from electronic medical records. Uni- and multivariate logistic regression analyses were used to identify independent risk factors of CG recurrence in the training set. The predictive nomogram was developed by incorporating these independent factors and histological subtype. The performance of the nomogram was assessed and validated with respects to its calibration, discrimination, and clinical usefulness. The risk of developing subsequent bladder cancer was analyzed from the follow-up data. RESULTS: Ultimately, 278 eligible patients were included and were allocated to a training set (n=190) and a validation set (n=88). Of them, 165 (59.35%) patients experienced CG recurrence, and none showed evidence of subsequent bladder carcinoma during a median (IQR) follow-up time of 27 months (14-57 months). Results of multivariate analysis showed that urinary infections, long-term indwelling catheter usage, urinary calculus, squamous metaplasia, and atypical hyperplasia were independent risk factors of CG recurrence. The C-index (95% CI) of the nomogram was 0.76 (0.69-0.83) in the training set and 0.72 (0.61-0.83) in the validation set. A decision curve analysis (DCA) demonstrated that this predictive nomogram was clinically useful. CONCLUSIONS: We developed and validated a nomogram to predict the individualized risk of CG recurrence. Also, we demonstrated that neither intestinal nor typical CG increased the consequent risk of bladder cancer during the follow-up period.

A nomogram incorporating PD-L1, NLR, and clinicopathologic features to predict inguinal lymph node metastasis in penile squamous cell carcinoma.

BACKGROUND: Accurate preoperative prediction of inguinal lymph node metastasis (LNM) aids in clinical decision making, especially for patients with penile cancer with clinically negative lymph nodes. We aim to develop a nomogram to predict the preoperative risk of LNM by incorporating clinicopathologic features and tumor biomarkers. METHODS: Eighty-four patients with penile cancer with clinically negative lymph nodes were enrolled. The programmed death ligand 1 (PD-L1) expression profile was detected by immunohistochemistry. The neutrophil-to-lymphocyte ratio (NLR) was calculated based on parameters of a routine blood examination. Multivariate logistic regression analysis was utilized to construct predictive nomograms for LNM based on data of 64 patients. The nomogram performance was assessed for calibration, discrimination, and clinical use. RESULTS: Tumor grade, lymphovascular invasion, PD-L1, and NLR were independent predictors of LNM. Then, 4 prediction models were constructed. Clinical model included tumor grade and lymphovascular invasion. NLR model was built by adding the NLR to clinical model. PD-L1 model was built by adding the PD-L1 to clinical model. Finally, a combined model was built by adding both PD-L1 and NLR to clinical model. Combined model showed the best performance compared with other models. It showed good discrimination with a C-index of 0.89, and good calibration. In addition, decision curve analysis suggested that model 4 was clinically useful. CONCLUSIONS: We developed a nomogram that incorporated tumor grade, lymphovascular invasion, PD-L1, and NLR that could be conveniently used to predict the preoperative individualized risk of inguinal LNM in patients with penile cancer.

A preoperative nomogram predicting the pseudocapsule status in localized renal cell carcinoma.

BACKGROUND: Tumor enucleation (TE) surgery for localized renal cell carcinoma (RCC) relies on a complete peritumoral pseudocapsule (PC). Study objective was to develop a preoperative model to predict PC status. METHODS: The prediction model was developed in a cohort that consisted of 170 patients with localized RCC, and data was gathered from 2010 to 2015. Multivariable logistic regression analysis and R were used to generate this prediction model. The statistical performance was assessed with respect to the calibration, discrimination, and clinical usefulness. RESULTS: The prediction model incorporated the systemic inflammatory markers [neutrophil-lymphocyte ratio (NLR); albumin-globulin ratio (AGR)], CT imaging features (tumor size and necrosis), and clinical risk factors (BMI). The model showed good discrimination, with a C-index of 0.85 (0.78-0.91), and good calibration (P=0.60). The sensitivity and specificity were 62% and 94% respectively. Decision curves and clinical impact curve demonstrated that the current model was clinically useful. CONCLUSIONS: We constructed a model that incorporated both the systematic inflammatory markers and clinical risk factors. It can be conveniently used to preoperatively predict the individualized risk of PC invasion and identify the best candidates to receive TE surgery.

Integrative Analysis of Sirtuins and Their Prognostic Significance in Clear Cell Renal Cell Carcinoma.

Sirtuins, class III histone deacetylases, are involved in multiple biological processes in cancer initiation and progression. However, the diverse expression patterns and prognostic values of sirtuins in cancers have yet to be elucidated. In this study, we first evaluated the expression and prognostic values of sirtuins in multiple cancer cohorts using publicly available TCGA pan-cancer datasets. Pan-cancer survival analysis indicated that 6 out of 7 sirtuin family members were significant associated with prognosis of clear cell renal cell carcinoma (KIRC) patients. SIRT1, SIRT3, SIRT4, and SIRT5 were associated with favorable prognosis of KIRC patients, while SIRT6 and SIRT7 were associated with unfavorable prognosis. The expression levels of SIRT4 and SIRT5 in KIRC tissues were lower than that in normal tissues, while SIRT6 and SIRT7 were higher in KIRC tissues. The expression levels of SIRT1, SIRT3, SIRT5, SIRT6, and SIRT7 were significantly correlated with tumor stage and histological grade. DNA methylation may contribute to the dysregulation of sirtuins. Finally, GSEA was conducted to predict the potential functions of sirtuins in KIRC. Our results may provide novel insights for the development of sirtuins-based cancer therapy in KIRC.

Integrated analysis of gene expression and DNA methylation profiles in ovarian cancer.

BACKGROUND: Ovarian cancer is an epithelial malignancy that intrigues people for its poor outcome and lack of efficient treatment, while methylation is an important mechanism that have been recognized in many malignancies. In this study, we attempt to assess abnormally methylated gene markers and pathways in ovarian cancer by integrating three microarray datasets. METHODS: Three datasets including expression (GSE26712 and GSE66957) and methylation (GSE81224) datasets were accessed. GEO2R platform was used to detect abnormally methylated-differentially expressed genes. Protein-protein interaction (PPI) networks were built and analysed for hypermethylated and hypermethylated differentially expressed genes using Cytoscape software and Mcode app. GEPIA and cBioPortal platforms were used to validate the expression of the hub genes and the correlation between their mRNA expressions and methylation levels. Kaplan Meier-plotter platform were used to assess the prognostic significance of the hub genes. RESULTS: Six hundred eighty-one hypomethylated-upregulated genes were detected and involved in Rap1 signaling pathway, biosynthesis of amino acids, endocrine resistance, apoptosis, pathways in cancer. The hub genes were TNF, UBC, SRC, ESR1, CDK1, PECAM1, CXCR4, MUC1, IKBKG. Additionally, 337 hypermethylated-downregulated genes were detected and involved in pathways in cancer, focal adhesion, sphingolipid signaling pathway, EGFR tyrosine kinase inhibitor resistance, cellular senescence. The hub genes were BDNF, CDC42, CD44, PPP2R5C, PTEN, UBB, BMP2, FOXO1, KLHL2. TNF, ESR1, MUC1, CD44, PPP2R5C, PTEN, UBB and FOXO1 showed significant negative correlation between their mRNA expressions and methylation levels. TNF, ESR1 and FOXO1 showed prognostic significance. CONCLUSIONS: Two novel gene networks were found for ovarian cancer. TNF, ESR1, MUC1 and FOXO1 are our candidate genes that might take part in ovarian cancer progression in an epigenetic approach, TNF, ESR1 and FOXO1 may serve as potential markers for ovarian cancer prognosis evaluation.