Background: Hepatocellular carcinoma (HCC) originates from the hepatocytes and accounts for 90% of liver cancer. The study intends to identify novel prognostic biomarkers for predicting the prognosis of HCC patients based on TCGA and GSE14520 cohorts. Methods: Differential analysis was employed to obtain the DEGs (Differentially Expressed Genes) of the TCGA-LIHC-TPM cohort. The lasso regression analysis was applied to build the prognosis model through using the TCGA cohort as the training group and the GSE14520 cohort as the testing group. Next, based on the prognosis model, we performed the following analyses: the survival analysis, the independent prognosis analysis, the clinical feature analysis, the mutation analysis, the immune cell infiltration analysis, the tumor microenvironment analysis, and the drug sensitivity analysis. Finally, the survival time of HCC patients was predicted by constructing nomograms. Results: Through the lasso regression analysis, we obtained a prognosis model of ten genes including BIRC5 (baculoviral IAP repeat containing 5), CDK4 (cyclin-dependent kinase 4), DCK (deoxycytidine kinase), HSPA4 (heat shock protein family A member 4), HSP90AA1 (heat shock protein 90 α family class A member 1), PSMD2 (Proteasome 26S Subunit Ubiquitin Receptor, Non-ATPase 2), IL1RN (interleukin 1 receptor antagonist), PGF (placental growth factor), SPP1 (secreted phosphoprotein 1), and STC2 (stanniocalcin 2). First, we found that the risk score is an independent prognosis factor and is related to the clinical features of HCC patients, covering AFP (α-fetoprotein) and stage. Second, we observed that the p53 mutation was the most obvious mutation between the high-risk and low-risk groups. Third, we also discovered that the risk score is related to some immune cells, covering B cells, T cells, dendritic, macrophages, neutrophils, etc. Fourth, the high-risk group possesses a lower TIDE score, a higher expression of immune checkpoints, and higher ESTIMATE score. Finally, nomograms include the clinical features and risk signatures, displaying the clinical utility of the signature in the survival prediction of HCC patients. Conclusions: Through the comprehensive analysis, we constructed an immune-related prognosis model to predict the survival of HCC patients. In addition to predicting the survival time of HCC patients, this model significantly correlates with the tumor microenvironment. Furthermore, we concluded that these ten immune-related genes (BIRC5, CDK4, DCK, HSPA4, HSP90AA1, PSMD2, IL1RN, PGF, SPP1, and STC2) serve as novel targets for antitumor immunity. Therefore, this study plays a significant role in exploring the clinical application of immune-related genes.
Background: Pancreatic carcinoma (PAAD) is one of the malignant tumors with high lethality and low survival rate. Armadillo (ARMCX) family members play vital roles in tumorigenesis, but the tumorigenesis was not unclear. Our study aims to explore the novel biomarker of early diagnosis, treatment, and prognosis for PAAD. Methods: Firstly, UCSC Xena was used to download the data of pan-cancer. Then, we perform the pan-cancer analysis of ARMCX genes from expression, survival, immune subtype, tumor microenvironment, and stemness. Secondly, we focus on the roles of ARMCX genes in PAAD from the immune subtype, tumor microenvironment, and stemness. Lastly, we used GEIPA to select a key gene, and ARMCX1 was selected as our object. We analyzed the roles of ARMCX1 in PAAD from the differential expression, survival, independent prognosis, clinical features, mechanism, DNA methylation levels, immune cell infiltration, and immunoinhabitors. Results: Based on the pan-cancer analysis, we deem that the differential expression of ARMCX genes exists in multiple tumors, which is closely associated with OS of multiple tumor patients. Plus, we also found that the expression is negatively related to immune subtypes, estimate score, immune score, stromal score, and stemness score. Based on the PAAD analysis, we found that the expression of ARMCX1 and ARMCX4 was significantly different in the immune subtype and a significant difference exists in RNAss, DNAss, stromal score, immune score, and estimate score. Based on the study of ARMCX1 in PAAD, We deemed that ARMCX1 is a lower expression in PAAD than in normal pancreas tissue, and the low expression is closely associated with poor OS of PAAD patients. ARMCX1 is an independent prognosis factor for PAAD patients. In addition, the downregulation of ARMCX1 is also closely associated with hypermethylation of the ARMCX1 promoter. We also found that the expression of ARMCX1 is related to some immune cells and immunoinhibitors. Conclusion: Therefore, we concluded that hypermethylation of the ARMCX1 promoter leads to the downregulation of ARMCX1 expression and inhabits the PAAD progression through influencing the immune cell by some potential mechanism.
BackgroundThe ability of cancer-associated fibroblasts (CAFs) to encourage angiogenesis, tumor cell spread, and increase treatment resistance makes them pro-tumorigenic. We aimed to investigate the CAF signature in Bladder urothelial carcinoma (BLCA) and, for clinical application, to build a CAF-based risk signature to decipher the immune landscape and screen for suitable treatment BLCA samples.MethodsCAF-related genes were discovered by superimposing CAF marker genes discovered from single-cell RNA-seq (scRNA-seq) data taken from the GEO database with CAF module genes discovered by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data from TCGA. After identifying prognostic genes related with CAF using univariate Cox regression, Lasso regression was used to build a risk signature. With microarray data from the GEO database, prognostic characteristics were externally verified. For high and low CAF-risk categories, immune cells and immunotherapy responses were analyzed. Finally, a nomogram model based on the risk signature and prospective chemotherapeutic drugs were examined.ResultsCombining scRNA-seq and bulk-seq data analysis yielded a total of 124 CAF-related genes. LRP1, ANXA5, SERPINE2, ECM1, RBP1, GJA1, and FKBP10 were the seven BLCA prognostic genes that remained after univariate Cox regression and LASSO regression analyses. Then, based on these genes, prognostic characteristics were created and validated to predict survival in BLCA patients. Additionally, risk signature had a strong correlation with known CAF scores, stromal scores, and certain immune cells. The CAF-risk signature was identified as an independent prognostic factor for BLCA using multifactorial analysis, and its usefulness in predicting immunotherapy response was confirmed. Based on risk classification, we projected six highly sensitive anticancer medicines for the high-risk group.ConclusionThe prognosis of BLCA may be accurately predicted using CAF-based risk signature. With a thorough understanding of the BLCA CAF-signature, it might be able to explain the BLCA patients’ response to immunotherapy and identify a potential target for BLCA treatment.
Background Previous studies have shown that the hypoxia microenvironment significantly impacted tumor progression. However, the clinical prognostic value of hypoxia-related risk signatures and their effects on the tumor microenvironment (TME) in hepatocellular carcinoma (HCC) remains hazy. This study aimed to conduct novel hypoxia-related prognostic signatures and improve HCC prognosis and treatment. Methods Differentially expressed hypoxia-related genes (HGs) were identified with the gene set enrichment analysis (GSEA). Univariate Cox regression was utilized to generate the tumor hypoxia-related prognostic signature, which consists of 3 HGs, based on the least absolute shrinkage and selection operator (LASSO) algorithm. Then the risk score for each patient was performed. The prognostic signature’s independent prognostic usefulness was confirmed, and systematic analyses were done on the relationships between the prognostic signature and immune cell infiltration, somatic cell mutation, medication sensitivity, and putative immunological checkpoints. Results A prognostic risk model of four HGs (FDPS, SRM, and NDRG1) was constructed and validated in the training, testing, and validation datasets. To determine the model’s performance in patients with HCC, Kaplan–Meier curves and time-dependent receiver operating characteristic (ROC) curves analysis was implemented. According to immune infiltration analysis, the high-risk group had a significant infiltration of CD4+ T cells, M0 macrophages, and dendritic cells (DCs) than those of the low-risk subtype. In addition, the presence of TP53 mutations in the high-risk group was higher, in which LY317615, PF−562271, Pyrimethamine, and Sunitinib were more sensitive. The CD86, LAIR1, and LGALS9 expression were upregulated in the high-risk subtype. Conclusions The hypoxia-related risk signature is a reliable predictive model for better clinical management of HCC patients and offers clinicians a holistic viewpoint when determining the diagnosis and course of HCC treatment.
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