Characterization of stemness features and construction of a stemness subtype classifier to predict survival and treatment responses in lung squamous cell carcinoma

Abstract Background Cancer stemness has been proven to affect tumorigenesis, metastasis, and drug resistance in various cancers, including lung squamous cell carcinoma (LUSC). We intended to develop a clinically applicable stemness subtype classifier that could assist physicians in predicting patient prognosis and treatment response. Methods This study collected RNA-seq data from TCGA and GEO databases to calculate transcriptional stemness indices (mRNAsi) using the one-class logistic regression machine learning algorithm. Unsupervised consensus clustering was conducted to identify a stemness-based classification. Immune infiltration analysis (ESTIMATE and ssGSEA algorithms) methods were used to investigate the immune infiltration status of different subtypes. Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenotype Score (IPS) were used to evaluate the immunotherapy response. The pRRophetic algorithm was used to estimate the efficiency of chemotherapeutic and targeted agents. Two machine learning algorithms (LASSO and RF) and multivariate logistic regression analysis were performed to construct a novel stemness-related classifier. Results We observed that patients in the high-mRNAsi group had a better prognosis than those in the low-mRNAsi group. Next, we identified 190 stemness-related differentially expressed genes (DEGs) that could categorize LUSC patients into two stemness subtypes. Patients in the stemness subtype B group with higher mRNAsi scores exhibited better overall survival (OS) than those in the stemness subtype A group. Immunotherapy prediction demonstrated that stemness subtype A has a better response to immune checkpoint inhibitors (ICIs). Furthermore, the drug response prediction indicated that stemness subtype A had a better response to chemotherapy but was more resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Finally, we constructed a nine-gene-based classifier to predict patients’ stemness subtype and validated it in independent GEO validation sets. The expression levels of these genes were also validated in clinical tumor specimens. Conclusion The stemness-related classifier could serve as a potential prognostic and treatment predictor and assist physicians in selecting effective treatment strategies for patients with LUSC in clinical practice

Preparation of the thienopyridine derivatives loaded liposomes and study on the effect of compound-lipid interaction on release behavior

The article describes characterization of two liposome formulations containing thienopyridine derivatives, namely TP-58 and TP-67. By preparing the liposomes, the concentration of the two compounds in ultrapure water was increased up to three orders of magnitude. After i.v. administration of the liposomes in rats, the initial compound plasma concentrations were enhanced more than fifty times relative to that after i.g. administration of the compound suspensions. It was found out that the release rate of TP-67 from the liposome both in vitro and in vivo was not significantly different from that of TP-58. TP-58 was more lipophilic than TP-67 according to partition coefficiency, and TP-67 had greater polarity than TP-58 based on polar surface area (PSA). With DSC, it was found out that the interaction magnitude between TP-67 and the lipid bilayer was not significantly different from that between TP-58 and the lipid bilayer, which accounted for the similarity of the two compounds in release rate both in vitro and in vivo. It indicated liposome can be used as a potential carrier for broading the application of TP-58 and TP-67. Interaction between the thienopyridine derivatives and the lipid bilayer is probably the decisive factor for compound release from the liposomes