Loss of OBSCN expression promotes bladder cancer progression but enhances the efficacy of PD-L1 inhibitors.

Background: As the objective overall response rate to immune checkpoint inhibitors (ICIs) is less than 30% in late stage or metastatic bladder cancer (BLCA), elucidating the intrinsic mechanisms of immune evasion is of great importance for the discovery of predictive and prognostic biomarkers and the exploration of novel targets for intervention. Recent studies have shown that OBSCN and the cytoskeletal protein it encodes, obscurin, play an important role in tumour progression. However, no studies have reported the role of OBSCN in BLCA.

Glycogen metabolism genes as a molecular signature for subtyping, prognostic prediction, and immunotherapy selection in clear cell renal cell carcinoma.

Glycogen accumulation is a typical feature in clear cell renal cell carcinoma (ccRCC). It has been reported that glycogen metabolism-related genes can promote the progression of ccRCC, but its role in molecular typing, prognosis, immune infiltration, and immunotherapy response has rarely been reported. We applied an unsupervised clustering approach for molecular typing of ccRCC.

GXYLT2: an emerging therapeutic target and predictive biomarker for anti-PD-1 efficacy in clear cell renal cell carcinoma.

There are studies reporting that glucoside xylosyltransferase 2 (GXYLT2) has a role in promoting tumor progression, but its role in clear cell renal cell carcinoma (ccRCC) remains unclear. In this study, RT-qPCR and western blotting were employed to detect the expression level of GXYLT2. RNA interference assays were used to knock down GXYLT2.

ALK-positive large B-cell lymphoma: a clinicopathological and molecular characteristics analysis of seven cases.

Anaplastic lymphoma kinase-positive large B-cell lymphoma (ALK LBCL) is a rare and highly aggressive lymphoma with characteristic ALK rearrangements. Various fusion genes involving ALK have been demonstrated, but the influence of the ALK fusion partners on ALK protein expression and the genetic characteristics of ALK LBCL remain relatively unknown. In this study, we conducted an extensive clinicopathological and molecular analysis on seven cases of ALK LBCL to explore the correlation between ALK fusion genes and ALK protein expression, thereby enriching the genetic characteristics of this tumour.

Establishment and identification of bladder cancer cell sheet.

Cell sheet technology (CST) has primarily been applied in tissue engineering for repair purposes. Our preliminary research indicates that an in vivo prostate cancer model established using CST outperforms traditional cell suspension methods. However, the potential for CST to be used with bladder cancer cells has not yet been explored.

Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.

Background: Metastasis renal cell carcinoma (RCC) patients have extremely high mortality rate. A predictive model for RCC micrometastasis based on pathomics could be beneficial for clinicians to make treatment decisions.

Methods: A total of 895 formalin-fixed and paraffin-embedded whole slide images (WSIs) derived from three cohorts, including Shanghai General Hospital (SGH), Clinical Proteomic Tumor Analysis Consortium (CPTAC) and Cancer Genome Atlas (TCGA) cohorts, and another 588 frozen section WSIs from TCGA dataset were involved in the study.

Dynamic changes of SCGN expression imply different phases of clear cell renal cell carcinoma progression.

The secretagogin (SCGN) was originally identified as a secreted calcium-binding protein present in the cytoplasm. Recent studies have found that SCGN has a close relationship with cancer. However, its role in the occurrence, progression, and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear.

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study.

Background: Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information into a single signature is necessary to stratify patients with clear cell renal cell carcinoma (ccRCC) for adjuvant therapy after surgery.

Methods: A total of 414 patients with whole slide images, computed tomography images, and clinical data from three patient cohorts were retrospectively analyzed. The authors performed deep learning and machine learning algorithm to construct three single-modality prediction models for disease-free survival of ccRCC based on whole slide images, cell segmentation, and computed tomography images, respectively.

Targeting lipid biosynthesis on the basis of conventional treatments for clear cell renal cell carcinoma: A promising therapeutic approach.

A variety of cancer cells exhibit dysregulated lipid metabolism, characterized by excessive intracellular lipid accumulation, and clear cell renal cell carcinoma (ccRCC) is the most typical disease with these characteristics. As the most common malignancy of all renal cell carcinomas (RCCs), ccRCC is typically characterized by a large accumulation of lipids and glycogen in the cytoplasm and a nucleus that is squeezed by the accumulated lipid droplets and localized to the marginal areas within the cytoplasm. This lipid accumulation has been found to be critically involved in the maintenance of malignant features observed in various cancers.

Artificial intelligence-based non-invasive tumor segmentation, grade stratification and prognosis prediction for clear-cell renal-cell carcinoma.

Due to the complicated histopathological characteristics of clear-cell renal-cell carcinoma (ccRCC), non-invasive prognosis before operative treatment is crucial in selecting the appropriate treatment. A total of 126 345 computerized tomography (CT) images from four independent patient cohorts were included for analysis in this study. We propose a V Bottleneck multi-resolution and focus-organ network (VB-MrFo-Net) using a cascade framework for deep learning analysis.

USP31 serves as a potential biomarker for predicting prognosis and immune responses for clear cell renal cell carcinoma via single-cell and bulk RNA-sequencing.

Background: Currently, there is no research available on the prognosis, potential effect and therapeutic value of USP31 in clear cell renal cell carcinoma (ccRCC). To address this gap, the present study aimed to shed light on its potential roles and possible mechanisms in ccRCC.

Methods: R software was utilized to conduct bioinformatics analyses with the data derived from The Cancer Genome Atlas (i.

Development and verification of a deep learning-based mA modification model for clinical prognosis prediction of renal cell carcinoma.

Background: The deep learning-based mA modification model for clinical prognosis prediction of patients with renal cell carcinoma (RCC) had not been reported for now. In addition, the important roles of methyltransferase-like 14 (METTL14) in RCC have never been fully explored.

Methods: A high-level neural network based on deep learning algorithm was applied to construct the mA-prognosis model.

A machine learning model based on MRI for the preoperative prediction of bladder cancer invasion depth.

Objectives: To construct and validate a prediction model based on full-sequence MRI for preoperatively evaluating the invasion depth of bladder cancer.

Methods: A total of 445 patients with bladder cancer were divided into a seven-to-three training set and test set for each group. The radiomic features of lesions were extracted automatically from the preoperative MRI images.

Screening and identification of prognostic genes associated with eosinophilic features of clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma, which is characterized by transparent cytoplasm. However, some ccRCC also show eosinophilic cytoplasm, and the molecular difference between eosinophilic and clear subtypes is unclear. In this study, we uncovered that under an optical microscope ccRCC with eosinophilic features has a poor prognosis.

Histone demethylase KDM4B contributes to advanced clear cell renal carcinoma and association with copy number variations and cell cycle progression.

Advanced renal cell carcinoma (RCC) poses a threat to patient survival. Epigenetic remodelling is the pathogenesis of renal cancer. Histone demethylase 4B (KDM4B) is overexpressed in many cancers through various pathways.

Integrative analysis of ferroptosis regulators for clinical prognosis based on deep learning and potential chemotherapy sensitivity of prostate cancer.

Exploring useful prognostic markers and developing a robust prognostic model for patients with prostate cancer are crucial for clinical practice. We applied a deep learning algorithm to construct a prognostic model and proposed the deep learning-based ferroptosis score (DLF) for the prediction of prognosis and potential chemotherapy sensitivity in prostate cancer. Based on this prognostic model, there was a statistically significant difference in the disease-free survival probability between patients with high and low DLF in the The Cancer Genome Atlas (TCGA) cohort ( < 0.

Cuproptosis-Related Genes Are Associated with Cell Cycle and Serve as the Prognostic Signature for Clear Cell Renal Cell Carcinoma.

Cuproptosis is a newly discovered type of cell death. The role and potential mechanism of Cuproptosis-related genes (CRGs) in the prognosis of cancer patients are not fully understood. In this study, we included two cohorts of clear cell renal cell carcinoma patients, TCGA and E-MTAB-1980.

HIF1A predicts the efficacy of anti-PD-1 therapy in advanced clear cell renal cell carcinoma.

Immunotherapy for cancer has become a revolutionary treatment, with the progress of immunological research on cancer. Cancer patients have also become more diversified in drug selection. Individualized medical care of patients is more important in the era of precision medicine.

Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma.

The sole clinicopathological characteristic is not enough for the prediction of survival of patients with clear cell renal cell carcinoma (ccRCC). However, the survival prediction model constructed by machine learning technology for patients with ccRCC using clinicopathological features is rarely reported yet. In this study, a total of 5878 patients diagnosed as ccRCC from four independent patient cohorts were recruited.

Lactic Acid Metabolism and Transporter Related Three Genes Predict the Prognosis of Patients with Clear Cell Renal Cell Carcinoma.

Lactic acid was previously considered a waste product of glycolysis, and has now become a key metabolite for cancer development, maintenance and metastasis. So far, numerous studies have confirmed that tumor lactic acid levels are associated with increased metastasis, tumor recurrence and poor prognosis. However, the prognostic value of lactic acid metabolism and transporter related genes in patients with clear cell renal cell carcinoma has not been explored.

The Dual Role of USP11 in Cancer.

Ubiquitination is one of the most crucial ways of protein degradation and plays an indispensable role in various living activities of cells. The deubiquitinating enzyme (DUB) is the main practitioner of the reversal of ubiquitination. Up till the present moment, nearly 100 DUBs from six families have been confirmed.

Robust Prediction of Prognosis and Immunotherapeutic Response for Clear Cell Renal Cell Carcinoma Through Deep Learning Algorithm.

It is of great urgency to explore useful prognostic markers and develop a robust prognostic model for patients with clear-cell renal cell carcinoma (ccRCC). Three independent patient cohorts were included in this study. We applied a high-level neural network based on TensorFlow to construct the robust model by using the deep learning algorithm.

A novel ferroptosis-related gene signature associated with cell cycle for prognosis prediction in patients with clear cell renal cell carcinoma.

Background: It is of great urgency to explore useful prognostic markers for patients with clear cell renal cell carcinoma (ccRCC). Prognostic models based on ferroptosis-related gene (FRG) in ccRCC is poorly reported for now.

Methods: Comprehensive analysis of 22 FRGs were performed in 629 ccRCC samples from two independent patient cohorts.