Simsjochumsen6699

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease defined by the absence of estrogen receptor (ER) and progesterone receptor (PR) expression, and human epidermal growth factor receptor 2 (HER2) overexpression that lacks targeted treatments, leading to dismal clinical outcomes. Thus, better stratification systems that reflect intrinsic and clinically useful differences between TNBC tumors will sharpen the treatment approaches and improve clinical outcomes. The lack of a rational classification system for TNBC also impacts current and emerging therapeutic alternatives. In the past years, several new methodologies to stratify TNBC have arisen thanks to the implementation of microarray technology, high-throughput sequencing, and bioinformatic methods, exponentially increasing the amount of genomic, epigenomic, transcriptomic, and proteomic information available. Thus, new TNBC subtypes are being characterized with the promise to advance the treatment of this challenging disease. However, the diverse nature of the molecular data, the poor integration between the various methods, and the lack of cost-effective methods for systematic classification have hampered the widespread implementation of these promising developments. However, the advent of artificial intelligence applied to translational oncology promises to bring light into definitive TNBC subtypes. This review provides a comprehensive summary of the available classification strategies. It includes evaluating the overlap between the molecular, immunohistochemical, and clinical characteristics between these approaches and a perspective about the increasing applications of artificial intelligence to identify definitive and clinically relevant TNBC subtypes.Gut microbiota dysbiosis is related to cancer development and progression. Our previous study showed that Ruminococcus was more abundant in CRPC (Castration-resistant prostate cancer) than HSPC (Hormone-sensitive prostate cancer) individuals. Here, we determined the potential mechanism of microbiota dysbiosis in prostate cancer (PCa) progression. Metagenomics was used to verify the gut microbial discrepancies between CRPC and HSPC individuals. Fecal microbiota transplantation (FMT) was performed by transferring the fecal suspension of CRPC or HSPC individuals to TRAMP mice. Afterwards, the mice's prostate histopathology and gut microbiota composition were determined. Since Ruminococcus was demonstrated to correlate with phospholipid metabolism, we used lipidomics to examine the mice's fecal lipid profiles. The expression of LPCAT1 the key enzyme for phospholipid remodeling in mice prostate was also examined. Meanwhile, both microbial functions prediction and LPCAT1 GSEA analysis (Gene Set Enrichment Analysis) indicated DNA repair pathways, we further determined the expressions of RAD51 and DNA-PKcs in mice prostate. The results showed that gut Ruminococcus was significantly more abundant in CRPC individuals. FMT using CRPC feces accelerated mice's PCa progression and increased their gut Ruminococcus abundance. Majority of fecal lipids including lysophosphatidylcholine and phosphatidylcholine were upregulated in CRPC FMT treated mice, accompanied with enhanced expressions of LPCAT1, RAD51, and DNA-PKcs in mice prostate. We reported an abundant colonization of Ruminococcus in the gut of CRPC individuals and mice receiving their fecal suspensions, and revealed the promotive capability of Ruminococcus in PCa progression via upregulating LPCAT1 and DNA repair protein expressions. The bacterium and its downstream pathways may become the targets of therapies for PCa in the future.

Paragangliomas (PGLs) are neuroendocrine neoplasms arising from chromaffin cells of sympathetic or parasympathetic paraganglia. Systemic therapies have been used only in metastatic PGLs. Antiangiogenic agents, such as sunitinib, could be a viable therapeutic choice in the subgroup of patients with

-positive PGLs. We describe the case of a man with Familial Paraganglioma Syndrome type 1 (FPGL) related to a novel mutation in

gene treated with sunitinib. Furthermore, we performed a systematic review of the literature aimed to address the following question is sunitinib treatment effective in patients with advanced/progressive/metastatic PGL?

We performed a data search using MEDLINE, Cochrane Library, and Scopus between April 2019 and September 2020. selleck chemicals llc We included studies reporting data on clinical or biological characteristics, or clinical outcomes of patients with PGLs treated with sunitinib.

The search leaded to the selection of 25 publications. Data from case reports and case series showed that disease control rate (DCR = stable disease + partial response + complete response) was achieved in 34.7% of cases under sunitinib treatment. In 39% of patients DCR was followed by progressive disease (PD) or tumor relapse, 26.1% patients showed PD. Data from clinical trials showed that DCR was 83%, and the median progression free survival was 13.4 months.

Data from the present literature review suggested that sunitinib could be a viable therapeutic option in advanced/progressive/metastatic inoperable PGLs. However, further trials on the efficacy of sunitinib in FPGL and sporadic PGL are needed.

Data from the present literature review suggested that sunitinib could be a viable therapeutic option in advanced/progressive/metastatic inoperable PGLs. However, further trials on the efficacy of sunitinib in FPGL and sporadic PGL are needed.

Hypoxia is associated with the development of pancreatic cancer (PC). However, genes associated with hypoxia response and their regulatory mechanism in PC cells were unclear. The current study aims to investigate the role of the hypoxia associated gene fucosyltransferase 11 (FUT11) in the progression of PC.

In the preliminary study, bioinformatics analysis predicted FUT11 as a key hypoxia associated gene in PC. The expression of FUT11 in PC was evaluated using quantitative real-time PCR (qRT-PCR), Western blot and immunohistochemistry. The effects of FUT11 on PC cells proliferation and migration under normoxia and hypoxia were evaluated using Cell Counting Kit 8, 5-ethynyl-2'-deoxyuridine (EDU) assay, colony formation assay and transwell assay. The effects of FUT11

was examined in mouse tumor models of liver metastasis and subcutaneous xenograft. Furthermore, Western blot, luciferase assay and immunoprecipitation were performed to explore the regulatory relationship among FUT11, hypoxia-inducible factor 1α (HIF1α) and pyruvate dehydrogenase kinase 1 (PDK1) in PC.