Introduction
Immune checkpoint inhibitors (ICIs) have become a frontier in the field of clinical technology for advanced non-small cell lung cancer (NSCLC). Currently, the predictive biomarker of ICIs mainly including the expression of PD-L1, TMB, TIICs, MMR and MSI-H. However, there are no official biomarkers to guide the treatment of ICIs and to determine the prognosis. Therefore, it is essential to explore a systematic nomogram to predict the prognosis of ICIs treatment in NSCLC
Methods
In this work, we obtained gene expression and clinical data of NSCLC patients from the TCGA database. Immune-related genes (IRGs) were downloaded from the ImmPort database. The detailed clinical annotation and response data of 240 advanced NSCLC patients who received ICIs treatment were obtained from the cBioPortal for Cancer Genomics. Kaplan–Meier survival analysis was used to perform survival analyses, and selected clinical variables to develop a novel nomogram. The prognostic significance of FGFR4 was validated by another cohort in cBioPortal for Cancer Genomics.
Results
3% of the NSCLC patients harbored FGFR4 mutations. The mutation of FGFR4 were confirmed to be associated with PD-L1, and TMB. Patients harbored FGFR4 mutations were found to have a better prolonged progression-free survival (PFS) to ICIs treatment (FGFR4: P = 0.0209). Here, we built and verified a novel nomogram to predict the prognosis of ICIs treatment for NSCLC patients.
Conclusion
Our results showed that FGFR4 could serve as novel biomarkers to predict the prognosis of ICIs treatment of advanced NSCLC. Our systematic prognostic nomogram showed a great potential to predict the prognosis of ICIs for advanced NSCLC patients.
Hepatocellular carcinoma (HCC) is one of the major causes of cancer-related death worldwide. AHSA1 as a chaperone of HSP90 promotes the maturation, stability, and degradation of related cancer-promoting proteins. However, the regulatory mechanism and biological function of AHSA1 in HCC are largely unknown. Actually, we found that AHSA1 was significantly upregulated in HCC tissues and cell lines and was notably correlated with the poor clinical characteristics and prognosis of HCC patients in this study. Furthermore, both in vitro and in vivo, gain- and loss-of-function studies demonstrated that AHSA1 promoted the proliferation, invasion, metastasis, and epithelial-mesenchymal transition (EMT) of HCC. Moreover, the mechanistic study indicated that AHSA1 recruited ERK1/2 and promoted the phosphorylation and inactivation of CALD1, while ERK1/2 phosphorylation inhibitor SCH772984 reversed the role of AHSA1 in the proliferation and EMT of HCC. Furthermore, we demonstrated that the knockdown of CALD1 reversed the inhibition of proliferation and EMT by knocking AHSA1 in HCC. We also illustrated a new molecular mechanism associated with AHSA1 in HCC that was independent of HSP90 and MEK1/2. In summary, AHSA1 may play an oncogenic role in HCC by regulating ERK/CALD1 axis and may serve as a novel therapeutic target for HCC.
Hepatocellular Carcinoma (HCC) is a type of liver cancer which is characterized by inflammation-associated tumor. The unique characteristics of tumor immune microenvironment in HCC contribute to hepatocarcinogenesis. It was also clarified that aberrant fatty acid metabolism (FAM) might accelerate tumor growth and metastasis of HCC. In this study, we aimed to identify fatty acid metabolism-related clusters and establish a novel prognostic risk model in HCC. Gene expression and corresponding clinical data were searched from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) portal. From the TCGA database, by unsupervised clustering method, we determined three FAM clusters and two gene clusters with distinct clinicopathological and immune characteristics. Based on 79 prognostic genes identified from 190 differentially expressed genes (DEGs) among three FAM clusters, five prognostic DEGs (CCDC112, TRNP1, CFL1, CYB5D2, and SLC22A1) were determined to construct risk model by least absolute shrinkage and selection operator (LASSO) and multivariate cox regression analysis. Furthermore, the ICGC dataset was used to validate the model. In conclusion, the prognostic risk model constructed in this study exhibited excellent indicator performance of overall survival, clinical feature, and immune cell infiltration, which has the potential to be an effective biomarker for HCC immunotherapy.
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