ABSTRACT
Background: Non-small cell lung cancer (NSCLC) accounts for a great number of all lung cancer cases. Hypoxia, one of the hallmarks in solid cancer, is closely involved in cancer cell progression and migration. This study aimed to develop a molecular subtyping system based on hypoxia-related genes and construct a prognostic model for NSCLC patients. Methods: Unsupervised consensus clustering was used to classify molecular subtypes. Mutation and immune analyses were conducted to compare differences among the molecular subtypes. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO) analysis, and step Akaike information criterion (stepAIC) were performed to screen prognostic genes. Results: Two molecular subtypes (C1 and C2) were identified based on hypoxia-related genes and showed significant differences in survival, enriched pathways, tumor microenvironment (TME), and sensitivity to immunotherapy and chemotherapy. Interestingly, C1 subtype had better survival and response to targeted therapies. Oncogenic pathways, such as hypoxia, epithelial mesenchymal transition (EMT), NOTCH signaling, and p53 signaling pathways were more enriched in C2 subtype. A 6-gene prognostic model with robust ability was developed to classify NSCLC patients into high-risk and low-risk groups. Conclusion: The novel molecular subtypes could assist personalized therapies to select suitable patients. The six prognostic genes may be novel targets for further understanding mechanisms of NSCLC development associated with hypoxia and exploiting novel targeted therapies.
ABSTRACT
Several effective constituents, such as vanillin, ferulic acid, senkyunolide I, senkyunolide H, coniferyl ferulate, Z-ligustilide, butylphthalide, senkyunolide A, and levistilide A, are unstable and possess mutual transformation relationships in Chuanxiong Rhizoma (CR). Traditional Chinese medicine mainly involves decoction, and the content of effective constituents and antiplatelet aggregation bioactivity (AAB) in CR may vary with different decoction time (10 min, 20 min, 30 min, 40 min, 50 min, and 60 min). Here, we showed that coniferyl ferulate and levistilide A were detected in CR material, but not in the decoction. The effective components possessed transformation and degradation in CR decoction of different times. The effective components and the strength of AAB at 10 and 20 minutes were the strongest, followed by 30-50 minutes, and 60 minutes were the weakest by analysis of SIMCA-PLS in CR decoction of different times. In the Pearson correlation analysis, there were correlations (P < 0.05) between effective components, which were ferulic acid and senkyunolide I (coefficient was 0.976), ferulic acid and senkyunolide H (coefficient was 0.972), senkyunolide I and senkyunolide H (coefficient was 0.982), senkyunolide A and butylphthalide (coefficient was 0.974), senkyunolide A and Z-ligustilide (coefficient was 0.947), and butylphthalide and Z-ligustilide (coefficient was 0.993). Effective components (ferulic acid, senkyunolide I, and senkyunolide H) and AAB were correlated and the Pearson correlation coefficients were respectively 0.965, 0.973, and 0.999. In the stepwise regression analysis, senkyunolide H and senkyunolide I were correlated with AAB (P < 0.05). Senkyunolide H (H) was positively correlated with AAB, senkyunolide I (I) was negatively correlated with AAB, and its expression was AAB = 1.187 ∗ H - 0.199 ∗ I - 0.422. These findings indicate that there are some correlations between effective components and AAB in CR.
