Conditional survival of patients with primary bone lymphoma of the spine: how survival changes after initial diagnosis.

Background: The current survival prediction methodologies for primary bone lymphoma (PBL) of the spine are deficient. This study represents the inaugural utilization of conditional survival (CS) to assess the outcome of this disease. Moreover, our objective was to devise a CS-based nomogram for predicting overall survival (OS) in real-time for spinal PBL. Methods: Patients with PBL of the spine diagnosed between January 2000 and December 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The OS was determined through the Kaplan-Meier method. The CS characteristic of patients with spinal PBL was delineated, with the CS being estimated utilizing the formula: CS(α|ß) = OS(α+ß)/OS(ß). CS(α|ß) denotes the probability of additional α-year survivorship, assuming the patient has already survived ß years after the time of observation. Three methods including univariate Cox regression, best subset regression (BSR) and the least absolute shrinkage and selection operator (LASSO) regression were used to identify predictors for CS-based nomogram construction. Results: Kaplan-Meier analysis was executed to determine the OS rate for these patients, revealing a survival rate of 68% and subsequently 63% at the 3-year and 5-year mark respectively. We then investigated the CS patterning exhibited by these patients and discovered the survival of PBL in the spine progressively improved with time. Meanwhile, through three different prognostic factor selection methods, we identified the best predicter subset including age, tumor histology, tumor stage, chemotherapy and marital status, for survival prediction model construction. Finally, we successfully established and validated a novel CS-based nomogram model for real-time and dynamic survival estimation. Moreover, we further designed a risk stratification system to facilitate the identification of high-risk patients. Conclusions: This is the first study to analyze the CS pattern of PBL of the spine. And we have also developed a CS-based nomogram that provide dynamic prognostic data in real-time, thereby aiding in the formulation of personalized treatment strategies in clinical practice.

Risk factors for intraprocedural hypoxemia in patients with acute cerebral ischemia treated with vascular intervention and its impact on prognosis: A retrospective cohort study.

BACKGROUND: Acute cerebral infarction (ACI) is one of the most common ischemic cerebrovascular diseases in neurology, with high morbidity, mortality, and disability. Early thrombolytic treatment of ACI has significant efficacy, but intraprocedural complications of hypoxemia can significantly reduce the efficacy. This study aims to analyze the risk factors for intraprocedural hypoxemia in patients with ACI, so as to take effective measures in advance to reduce the likelihood of adverse patient outcomes. METHODS: We retrospectively analyzed a total of 238 patients with ACI treated with vascular interventions from May 2017 to May 2022. To assess and collate the patients' characteristics, factors associated with the development of intraprocedural hypoxemia. The independent risk factors for the development of intraprocedural hypoxemia were analyzed by binary logistic regression. RESULTS: A total of 238 patients were included in this study. Of these, intraprocedural hypoxemia occurred in 89 (37.4%). The results showed that old age (odds ratio [OR] = 2.666, P = 0.009), obesity (OR = 3.029, P = 0.003), smoking history (OR = 2.655, P = 0.010), preoperative oxygen saturation (SpO2) (OR = 0.001, P = 0.042), preoperative C-reactive protein (OR = 1.216, P = 0.002), and time from puncture to vascular recanalization (OR = 1.135, P = 0.000) were independent risk factors for intraprocedural hypoxemia in patients. The prognosis of the patients was assessed according to the modified Rankin scale, and the prognosis of the nonhypoxemia group was significantly better than that of the hypoxemia group. Regression analysis showed that intraprocedural hypoxemia (OR = 0.360, P = 0.001), postoperative lower extremity vein thrombosis (OR = 0.187, P = 0.018), hydrocephalus (OR = 0.069, P = 0.015), intracranial hemorrhage (OR = 0.116, P = 0.002), and reocclusion (OR = 0.217, P = 0.036) were independent risk factors for poor prognosis. CONCLUSIONS: Currently, intravascular hypoxemia in patients with ACI has a serious impact on prognosis. Clinical work should attach great importance to the clinical characteristics of patients, identify relevant risk factors, and aggressively take personalized therapeutic actions to improve patients' prognosis.

Conditional survival analysis and dynamic survival prediction for intracranial solitary-fibrous tumor/hemangiopericytoma.

BACKGROUND: As the form of World Health Organization Central Nervous System (WHO CNS) tumor classifications is updated, there is a lack of research on outcomes for intracranial combined solitary-fibrous tumor and hemangiopericytoma (SFT/HPC). This study aimed to explore conditional survival (CS) pattern and develop a survival prediction tool for intracranial SFT/HPC patients. METHODS: Data of intracranial SFT/HPC patients was gathered from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. The patients were split into training and validation groups at a 7:3 ratio for our analysis. CS is defined as the likelihood of surviving for a specified period of time (y years), given that the patient has survived x years after initial diagnosis. Then, we used this definition of CS to analyze the intracranial SFT/HPC patients. The least absolute shrinkage and selection operator (LASSO) regression and best subset regression (BSR) were employed to identify predictive factors. The Multivariate Cox regression analysis was applied to establish a novel CS-based nomogram, and a risk stratification system was developed using this model. RESULTS: From the SEER database, 401 patients who were diagnosed with intracranial SFT/HPC between 2000 and 2019 were identified. Among them, 280 were included in the training group and 121 were included in the internal validation group for analysis. Our study revealed that in intracranial SFT/HPC, 5-year survival rates saw significant improvement ranging from 78% at initial diagnosis to rates of 83%, 87%, 90%, and 95% with each successive year after surviving for 1-4 years. The LASSO regression and BSR identified patient age, tumor behavior, surgery and radiotherapy as predictors of CS-based nomogram development. A risk stratification system was also successfully constructed to facilitate the identification of high-risk patients. CONCLUSION: The CS pattern of intracranial SFT/HPC patients was outlined, revealing a notable improvement in 5-year survival rates after an added period of survival. Our newly-established CS-based nomogram and risk stratification system can provide a real-time dynamic survival estimation and facilitate the identification of high-risk patients, allowing clinicians to better guide treatment decision for these patients.

Decision Tree Model for Predicting the Overall Survival of Patients with Diffused Large B-Cell Lymphoma in the Central Nervous System.

OBJECTIVE: To identify the significant predictors of overall survival for patients living with diffused large B-cell lymphoma (DLBCL) in the central nervous system and establish a novel decision tree model to help predict survival status at several time points. METHODS: Patients diagnosed with DLBCL were identified from the SEER database and randomly divided into training and test samples (6:4). Dichotomous decision trees were developed for survival status at 3, 12, 24, and 60 months. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy rate, and area under the receiver operating characteristic curve were calculated to evaluate the model performance. RESULTS: A total of 2998 patients were included, with 1799 and 1199 patients divided into the training and testing groups. Decision trees for 3, 12, 24, and 60 months survival status were generated. Chemotherapy and patient's age were of the primary importance for prognosis in the novel models. Favorable consistency between the predicted and actual survival status was presented. The accuracy rates were 0.79, 0.71, 0.68, and 0.86 for training sample at 3, 12, 24, and 60 months, respectively, and 0.75, 0.69, 0.58, and 0.84 for test sample at 3, 12, 24, and 60 months, respectively. The area under the receiver operating characteristic curve values ranged between 0.645 and 0.721 for the training sample and between 0.607 and 0.712 for the test sample. CONCLUSIONS: Novel decision tree models were established for predicting the 3, 12, 24, and 60 months survival status of patients with DLBCL. The newly developed models were verified using training and test samples, showing favorable accuracy and predictive value on overall survival.

Downregulation of USP34 Inhibits the Growth and Migration of Pancreatic Cancer Cells via Inhibiting the PRR11.

BACKGROUND: Pancreatic cancer (PC) is a highly lethal malignancy worldwide. Our previous study indicated that overexpression of USP34 could promote tumor growth in PC cells. Therefore, this study aimed to further investigate the role of USP34 during the tumorigenesis of PC. METHODS: The level of USP34 in PANC-1 and MiaPaCa-2 cells transfected with USP34-shRNAs was detected by RT-qPCR. Moreover, transwell migration and Annexin V/PI analysis were conducted to detect cell migration and apoptosis, respectively. RESULTS: In this study, downregulation of USP34 markedly inhibited proliferation and migration, and induced apoptosis in PANC-1 cells. Moreover, silencing of USP34 obviously downregulated the levels of PRR11 and p-p38 in PANC-1 cells. An in vivo study in nude mice bearing PANC-1 cell xenografts confirmed these results. CONCLUSION: Downregulation of USP34 could inhibit proliferation and migration in PANC-1 cells via inhibiting PRR11, and inactivating p38 MAPK signaling. Therefore, USP34 might be a potential therapeutic target for the treatment of PC.

USP34 Regulated Human Pancreatic Cancer Cell Survival via AKT and PKC Pathways.

Pancreatic cancer is known to be a fatal disease, which is difficult to be diagnosed in its early stages. Ubiquitin-Specific Protease 34 (USP34) are closely related to human cancers in the development and progression. However, there are rarely studies about the role of USP34 in pancreatic cancer. Thus, we aimed to investigate the effect of USP34 in human pancreatic cancer. Short-hairpin RNA targeting USP34 (USP34-shRNA) and USP34 overexpression lentivirus were used in the current study. The level of USP34 in human pancreatic cancer (PANC-1) cells were then analyzed by quantitative (q)RT-PCR. In addition, Western blotting was used to examine phosphorylated (p)-AKT, p-protein kinase C (PKC) and p-extracellular signal-regulated kinase (ERK) protein levels. CCK-8 assay, flow cytometry, and migration assay were used to detect cell proliferation, apoptosis and migration, respectively in vitro. According to the result of qRT-PCR and Western blotting, USP34-shRNA1 significantly downregulated USP34 gene level in PANC-1 cell. Subsequently, Western blotting assay indicated that USP34 silencing significantly down-regulated the expression of p-AKT and p-PKC in cells. On the other hand, USP34 overexpressing remarkably up-regulated the expression of p-AKT and p-PKC in cells. In addition, USP34 overexpression promoted PANC-1 cell proliferation and migration via up-regulating the proteins of p-AKT and p-PKC. Moreover, USP34 overexpression reversed AKT inhibitor and PKC inhibitor induced PACN-1 cell apoptosis. Our results indicated USP34 regulated h PANC-1 cell survival via AKT and PKC pathways, and which played a pro-survival role in human pancreatic cancer. Therefore, we suggested USP34 could be a potential therapeutic target for pancreatic cancer.

Discovery of AG-120 (Ivosidenib): A First-in-Class Mutant IDH1 Inhibitor for the Treatment of IDH1 Mutant Cancers.

Somatic point mutations at a key arginine residue (R132) within the active site of the metabolic enzyme isocitrate dehydrogenase 1 (IDH1) confer a novel gain of function in cancer cells, resulting in the production of d-2-hydroxyglutarate (2-HG), an oncometabolite. Elevated 2-HG levels are implicated in epigenetic alterations and impaired cellular differentiation. IDH1 mutations have been described in an array of hematologic malignancies and solid tumors. Here, we report the discovery of AG-120 (ivosidenib), an inhibitor of the IDH1 mutant enzyme that exhibits profound 2-HG lowering in tumor models and the ability to effect differentiation of primary patient AML samples ex vivo. Preliminary data from phase 1 clinical trials enrolling patients with cancers harboring an IDH1 mutation indicate that AG-120 has an acceptable safety profile and clinical activity.