Recent patents on anti-cancer drug discovery最新文献

Background: Liver cancer (LC) is one of China's most common malignant tumors, with a high mortality rate, ranking third leading cause of death after gastric and esophageal cancer. Recent patents propose the LncRNA FAM83H-AS1 has been verified to perform a crucial role in the progression of LC. LncRNA FAM83H-AS1 has been verified to perform a crucial role in the progression of LC. However, the concrete mechanism remains to be pending further investigation.

Objective: This study aimed to explore the embedding mechanism of FAM83H-AS1 molecules in terms of radio sensitivity of LC and provide potentially effective therapeutic targets for LC therapy.

Methods: Quantitative real-time PCR (qRT-PCR) was conducted to measure the transcription levels of genes. Proliferation was determined via CCK8 and colony formation assays. Western blot was carried out to detect the relative protein expression. A xenograft mouse model was constructed to investigate the effect of LncRNA FAM83H-AS1 on tumor growth and radio-sensitivity in vivo.

Results: The levels of lncRNA FAM83H-AS1 were remarkably increased in LC. Knockdown of FAM83H-AS1 inhibited LC cell proliferation and colony survival fraction. Deletion of FAM83H-AS1 increased the sensitivity of LC cells to 4 Gy of X-ray radiation. In the xenograft model, radiotherapy combined with FAM83H-AS1 silencing significantly reduced tumor volume and weight. Overexpression of FAM83H reversed the effects of FAM83H-AS1 deletion on proliferation and colony survival fraction in LC cells. Moreover, the over-expressing of FAM83H also restored the tumor volume and weight reduction caused by the knockdown of FAM83H-AS1 or radiation in the xenograft model.

Conclusion: Knockdown of lncRNA FAM83H-AS1 inhibited LC growth and enhanced radiosensitivity in LC. It has the potential to be a promising target for LC therapy.

Introduction: Nowadays, mounting evidence shows that variations in TGF-β signaling pathway-related components influence tumor development. Current research has patents describing the use of anti-TGF-β antibodies and checkpoint inhibitors for the treatment of proliferative diseases. Importantly, TGF-β signaling pathway is significant for lower-grade glioma (LGG) to evade host immunity. Loss of particular tumor antigens and shutdown of professional antigenpresenting cell activity may render the anti-tumor response ineffective in LGG patients. However, the prognostic significance of TGF-β related genes in LGG is still unknown.

Methods: We collected RNA-seq data from the GTEx database (normal cortical tissues), the Cancer Genome Atlas database (TCGA-LGG), and the Chinese Glioma Genome Atlas database (CGGA-693 and CGGA-325) for conducting our investigation.

Results: In addition, previous publications were explored for the 223 regulators of the TGF-β signaling pathway, and 30 regulators with abnormal expression in TCGA and GTEx database were identified. In order to identify hub prognostic regulators, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to screen from differentially expressed genes (DEGs). On the basis of 11 genes from LASSO-Cox regression analysis (NEDD8, CHRD, TGFBR1, TP53, BMP2, LRRC32, THBS2, ID1, NOG, TNF, and SERPINE1), TGF-β score was calculated. Multiple statistical approaches verified the predictive value of the TGF-β score for the training cohort and two external validation cohorts. Considering the importance of the TGF-β signaling pathway in immune regulation, we evaluated the prediction of the TGF-β score for immunological characteristics and the possible application of the immunotherapeutic response using six algorithms (TIMER, CIBERSORT, QUANTISEQ, MCP-counter, XCELL and EPIC) and three immunotherapy cohorts (GSE78820, Imvigor-210 and PRJEB23709). Notably, we compared our risk signature with the signature in ten publications in the meta-cohort (TCGA-LGG, CGGA-693 and CGGA-325), and the TGF-β score had the best predictive efficiency (C-index =0.812).

Conclusion: In conclusion, our findings suggest that TGF-β signaling pathway-related signatures are prognostic biomarkers in LGG and provide a novel tool for tumor microenvironment (TME) assessment.

Objective: We aimed to identify critical clinical features to develop an accurate webbased prediction model for estimating the overall survival (OS) of primary breast diffuse large Bcell lymphoma (PB-DLBCL) adult patients.

Methods: We first included all PB-DLBCL cases with available covariates retrieved from the Surveillance, Epidemiology, and End Results database. We sequentially performed univariate and multivariate Cox regression approaches to identify the predictors independently associated with prognosis, and all the predictors that passed these tests were then constructed to build a nomogram for predicting 3-, 5-, and 10-year survival rates of patients. The C-index and the receiver operating characteristic curve (ROC) were used to evaluate the prediction discrimination, and the calibration curve was applied to estimate the calibration.

Results: A total of PB-DLBCL adult patients were included (median age was 69 with the interquartile range [IQR] of 57-79 years), of which 466 (70%) were randomly allocated to the development cohort, and the remaining cases were collected for validation. Using three identified independent predictors (i.e., age, stage, and radiation), an accurate nomogram for predicting OS was developed and validated. The C-indices of our nomogram were both relatively acceptable, with 0.74 (95% CI: 0.71-0.78) and 0.72 (95% CI: 0.70-0.75) for the development and validation cohorts, respectively. The calibration curves also accurately predicted the prognosis of PB-DLBCL in all cases. In addition, ROC curves showed our nomogram to possess superior predictive ability compared to any single variable. To visually present this prediction model, a convenient webbased tool was implemented based on our prognostic nomogram.

Conclusion: For patients with PB-DLBCL, a more convenient and accurate web-based prediction model was developed and validated, which showed relatively good performances in both discrimination and calibration during model development and validation. External evaluation and validation are warranted by further independent studies.

Background: Gefitinib, an Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor (EGFR-TKI), frequently causes side effects when used to treat non-small cell lung cancer.

Objective: The purpose of this experiment was to investigate the side effect of gefitinib on the skin and colon of mice.

Methods: Male Balb/c nu-nu nude mice aged 4-5 weeks were used as xenograft tumor models, and gefitinib at 150 mg/kg and 225 mg/kg was started at 9 days after the xenograft tumor grew out. The mice's weights and tumor volumes were tracked concurrently, and the mouse skin adverse reactions and diarrhea were observed during the treatment. The animal tissues were subjected to biochemical and pathological evaluations after 14 days.

Results: Gefitinib effectively decreased the size and weight of transplanted tumors in nude mice, while also lowering body weight and raising indexes of the liver and spleen. Gefitinib could cause skin adverse reactions and diarrhea in mice. Further pathological investigation revealed tight junction- related markers in the mice's skin and colon to be reduced and macrophages and neutrophils to be increased after gefitinib treatment.

Conclusion: The findings imply that gefitinib has negative effects on the skin and colon. Gefitinib- induced skin and colon adverse reactions in mice have been successfully modeled in this study.

Background: The purpose of this study was to explore the expression profiles of lipid metabolism-related genes in patients with Colorectal Cancer (CRC).

Methods: The lipid metabolism statuses of CRC patients from The Cancer Genome Atlas (TCGA) were analyzed. Risk characteristics were constructed by univariate Cox regression and minimum Absolute contraction and Selection Operator (LASSO) Cox regression. A histogram was constructed based on factors such as age, sex, TNM stage, T stage, N stage, and risk score to provide a visual tool for clinicians to predict the probability of 1-year, 3-year, and 5-year OS for CRC patients. By determining Area Under Curve (AUC) values, the time-dependent Receiver Operating characteristic Curve (ROC) was used to evaluate the efficiency of our model in predicting prognosis.

Results: A novel risk signal based on lipid metabolism-related genes was constructed to predict the survival of CRC patients. Risk characteristics were shown to be an independent prognostic factor in CRC patients (p <0.001). There were significant differences in the abundance and immune characteristics of tumor-filtering immune cells between high-risk and low-risk groups. The nomogram had a high potential for clinical application and the ROC AUC value was 0.827. Moreover, ROC analysis demonstrated that the nomogram model was more accurate to predict the survival of CRC patients than age, gender, stage and risk score.

Conclusion: In this study, we demonstrated a lipid metabolism-related genes prognosis biomarker associated with the tumor immune micro-environment in patients with CRC.

The patent describes novel useful compounds, such as PI3K protein kinase inhibitors, in particular as PI3K delta (δ) and/or gamma (γ) protein kinase modulators. The present disclosure also provides methods for preparing PI3K protein kinase inhibitors, pharmaceutical compositions containing them, and methods of treatment, prevention, and amelioration of PI3K kinase-mediated diseases, and disorders.

Cancer is one of the leading causes of mortality and morbidity worldwide, affecting millions of people physically and financially every year. Over time, many anticancer treatments have been proposed and studied, including synthetic compound consumption, surgical procedures, or grueling chemotherapy. Although these treatments have improved the daily life quality of patients and increased their survival rate and life expectancy, they have also shown significant drawbacks, including staggering costs, multiple side effects, and difficulty in compliance and adherence to treatment. Therefore, natural compounds have been considered a possible key to overcoming these problems in recent years, and thorough research has been done to assess their effectiveness. In these studies, scientists have discovered a meaningful interaction between several natural materials and signal transducer and activator of transcription 3 molecules. STAT3 is a transcriptional protein that is vital for cell growth and survival. Mechanistic studies have established that activated STAT3 can increase cancer cell proliferation and invasion while reducing anticancer immunity. Thus, inhibiting STAT3 signaling by natural compounds has become one of the favorite research topics and an attractive target for developing novel cancer treatments. In the present article, we intend to comprehensively review the latest knowledge about the effects of various organic compounds on inhibiting the STAT3 signaling pathway to cure different cancer diseases.