Tamoxifen is a drug commonly used in the treatment of breast cancer, especially for postmenopausal patients. However, its efficacy is limited by the development of drug resistance. Downregulation of estrogen receptor alpha (ERα) is an important mechanism of tamoxifen resistance. In recent years, with progress in research into the protective autophagy of drug-resistant cells and cell cycle regulators, major breakthroughs have been made in research on tamoxifen resistance. For a better understanding of the mechanism of tamoxifen resistance, protective autophagy, cell cycle regulators, and some transcription factors and enzymes regulating the expression of the estrogen receptor are summarized in this review. In addition, recent progress in reducing resistance to tamoxifen is reviewed. Finally, we discuss the possible research directions into tamoxifen resistance in the future to provide assistance for the clinical treatment of breast cancer.
Highlights
Abnormal alternative splicing is involve in abnormal expression of genes in cancer.
Abnormal alternative splicing events promote malignant progression of cancer.
Abnormal alternative splicing develops tumor resistance to targeted therapy by changing the target point and signal transduction pathway.
Abnormal alternative splicing develops tumor resistance to immunotherapy by changing cell surface antigens and protein structure.
Purpose: Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC. Methods: The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 cervical cancer samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and KEGG pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and HPA database to exhibit the differences in gene and protein levels of candidate genes. Results: A total of 313 DEGs were screened from the microarray expression profile datasets. DNMT1, CHAF1B, CHAF1A, MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC. Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA databases and qRT-PCR confirmed that these genes were highly expressed in CC tissues. Conclusion: The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and KNTC1, as potential diagnostic and prognostic biomarkers of CC.
There are few studies on the role of iron metabolism genes in predicting the prognosis of lung adenocarcinoma (LUAD). Therefore, our research aims to screen key genes and to establish a prognostic signature that can predict the overall survival rate of lung adenocarcinoma patients. RNA-Seq data and corresponding clinical materials of 594 adenocarcinoma patients from The Cancer Genome Atlas(TCGA) were downloaded. GSE42127 of Gene Expression Omnibus (GEO) database was further verified. The multi-gene prognostic signature was constructed by the Cox regression model of the Least Absolute Shrinkage and Selection Operator (LASSO). We constructed a prediction signature with 12 genes (HAVCR1, SPN, GAPDH, ANGPTL4, PRSS3, KRT8, LDHA, HMMR, SLC2A1, CYP24A1, LOXL2, TIMP1), and patients were split into high and low-risk groups. The survival graph results revealed that the survival prognosis between the high and lowrisk groups was significantly different (TCGA: P < 0.001, GEO: P = 0.001). Univariate and multivariate Cox regression analysis confirmed that the risk value is a predictor of patient OS (P < 0.001). The area under the time-dependent ROC curve (AUC) indicated that our signature had a relatively high true positive rate when predicting the 1-year, 3-year, and 5-year OS of the TCGA cohort, which was 0.735, 0.711, and 0.601, respectively. In addition, immune-related pathways were highlighted in the functional enrichment analysis. In conclusion, we developed and verified a 12-gene prognostic signature, which may be help predict the prognosis of lung adenocarcinoma and offer a variety of targeted options for the precise treatment of lung cancer.
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