Fibroblast growth factor receptor risk signature predicts patient prognosis and immunotherapy resistance in colorectal cancer.

Background: Fibroblast Growth Factor Receptor (FGFR) signaling is linked with tumor progression and tumor immunoevasion, yet the potential effect of FGFR signature on the prognosis of patient with colorectal cancer (CRC) and response to immune therapy remains elusive.

Methods: The fibroblast growth factor receptor risk signature (FRS) was identified through single-cell RNA sequencing, bulk RNA sequencing, and machine learning techniques. Signaling enrichment analyses were conducted using Gene Set Enrichment Analysis (GSEA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG).

Targeted paclitaxel prodrug nanoassemblies to improve therapeutic effects for liver cancer.

Prodrug nanoassemblies fabricated by anticancer drug conjugates exhibited more advantages in controlled drug release and bioavailability and favorable antitumor efficacy. In this paper, lactobionic acid (LA) was connected with polyethylene glycol through amido linkages, and paclitaxel was joined with polyethylene glycol by means of ester bonds to form the prodrug copolymer LA-PEG-PTX. Then, LA-PEG-PTX was automatically assembled into LA-PEG-PTX nanoparticles (LPP NPs) by dialysis.

Structural Optimization and Structure-Activity Relationship Studies of 6,6-Dimethyl-4-(phenylamino)-6-pyrimido[5,4-][1,4]oxazin-7(8)-one Derivatives as A New Class of Potent Inhibitors of Pan-Trk and Their Drug-Resistant Mutants.

Tropomyosin receptor kinases (TrkA, TrkB, and TrkC) are attractive therapeutic targets for multiple cancers. Two first-generation small-molecule Trks inhibitors, larotrectinib and entrectinib, have just been approved to use clinically. However, the drug-resistance mutations of Trks have already emerged, which calls for new-generation Trks inhibitors.

Codon optimization with deep learning to enhance protein expression.

Heterologous expression is the main approach for recombinant protein production ingenetic synthesis, for which codon optimization is necessary. The existing optimization methods are based on biological indexes. In this paper, we propose a novel codon optimization method based on deep learning.

[Clinical analysis of sudden deafness after radiotherapy and chemotherapy in nasopharyngeal carcinoma patients].

Objective: To investigate the clinical features and therapeutic effects of sudden deafness after radiotherapy combined with chemotherapy in nasopharyngeal carcinoma patients.

Method: Clinical data of 42 nasopharyngeal carcinoma patients suffered from sudden deafness after radiotherapy combined with chemotherapy were analyzed retrospectively. Among the 42 patients, 2 showed moderate deafness, 4 presented excessive deafness, 30 suffered from severe deafness, and 6 exhibited profound deafness.