RESUMO
Background: Globally, liver cancer is one of the most common malignant tumors and is the third leading cause of cancer deaths. RNA-binding protein (RBP) is a general term for a class of proteins that bind to RNA to regulate metabolic processes. The expression of RNA-binding proteins is related to the prognosis of liver cancer patients. Methods: The RBP gene expression data of liver cancer were extracted from the TCGA database. First, the differentially expressed RBPs (DE RBPs) were selected through enrichment analysis and volcano mapping. Then, the prognosis-related RBP genes were selected through single-factor Cox regression analysis. The key prognosis-related RBPs were further screened by multifactor Cox regression analysis, and a formula for the patient's risk coefficient was obtained. Finally, based on the patient's risk score, a nomogram was established and verified. Results: We extracted 374 cancer tissue samples and 50 normal tissue samples with the clinical information from each sample. Through enrichment analysis, we screened 208 upregulated RBPs and 122 downregulated RBPs. Prognosis-related high-risk genes were EEF1E1, NOP56, UPF3B, SF3B4, SMG5, CD3EAP, BRCA1, BARD1, XPO5, CSTF2, EZH2, EXO1, RRP12, PRIM1, LIN28B, NROB1 and TCOF1, and the low-risk genes were MRPL46, RCL1, MRPL54, CPEB3, IFIT5, PPARGC1A, EIF2AK4, SEPSECS, ACO1, SECISBP2 L and ZCCHC24. Further multivariate Cox regression analysis was performed on the prognosis-related RBPs, and the three key prognosis-related RBPs were screened out, which were BARD1, NR0B1 and EIF2AK4. A patient risk coefficient calculation formula was obtained: risk score = (1.207×BARD1 Exp) + (0.483×NR0B1 Exp) + (-0.720×EIF2AK4 Exp). Finally, a nomogram was established based on the risk score to predict the survival time of patients from 1 to 5 years. Conclusions: The nomogram has good predictive value for the survival time of liver cancer patients.
RESUMO
BACKGROUND: Colon cancer is one of the most common malignant tumors, with high rates of incidence and death. The tumor mutational burden (TMB), which is characterized by microsatellite instability, has been becoming a powerful predictor which can show tumor behavior and response to immunotherapy. METHODS: In this study, we analyzed 437 mutation data of colon cancer samples obtained from The Cancer Genome Atlas (TCGA) and divided patients into low- and high-TMB groups according to the TMB value. Then we identified differentially-expressed genes (DEGs), conducted immune cell infiltration and survival analyses between groups. RESULTS: The higher TMB of the patients with colon cancer predicts a poorer prognosis. Functional analysis was performed to assess the prognostic value of the top 30 core genes. The CIBER-SORT algorithm was used to investigate the correlation between the immune cells and TMB subtypes. An immune prognosis model was constructed to screen out immune genes related to prognosis, and the tumor immunity assessment resource (TIMER) was then used to determine the correlation between gene expression and the abundance of tumor-infiltrating immune cell subsets in colon cancer. We observed that APC, TP53, TTN, KRAS, MUC16, SYNE1, PIK3CA have higher somatic mutations. DEGs enrichment analysis showed that they are involved in the regulation of neuroactive ligand-receptor interaction, the Cyclic adenosine monophosphate (cAMP) signaling pathway, the calcium signaling pathway, and pantothenate and Coenzyme A (CoA) biosynthesis. The difference in the abundance of various white blood cell subtypes showed that Cluster of Differentiation 8 (CD8) T cells (P=0.008), activated CD4 memory T cells (P=0.019), M1 macrophages (P=0.002), follicular helper T cells (P=0.034), activated Natural killer (NK cell) cells (P=0.017) increased remarkably, while M0 macrophages significantly reduced (P=0.025). The two immune model genes showed that secretin (SCT) was negatively correlated with survival, while Guanylate cyclase activator 2A (GUCA2A) was positively correlated. CONCLUSIONS: This study conducted a systematically comprehensive analysis of the prediction and clinical significance of TMB in colon cancer in identification, monitoring, and prognosis of colon cancer, and providing reference information for immunotherapy.
