This study explores the core genes involved in the pathogenesis of ACTH-independent macronodular adrenal hyperplasia (AIMAH), so as to provide robust biomarkers for the clinical diagnosis and treatment of this disease. Gene Expression Omnibus (GEO) database was used to obtain GSE25031 microarray dataset. R package “limma” was applied to identify differentially expressed genes (DEGs) between AIMAH and normal samples. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was employed to perform Gene Ontology (GO) annotation for the DEGs, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted. A protein-protein interaction network (PPI) was constructed using the STRING online website and visualized using the Cytoscape software. The key modules and hub genes were then identified. Finally, Gene Set Enrichment Analysis (GESA) enrichment analysis was carried out to find the signaling pathways of significant clinical value in AIMAH. A total of 295 DEGs between AIMAH and healthy samples were screened out, including 164 upregulated genes and 131 downregulated genes. Combining enrichment analysis and PPI network construction, there were 5 signifiant pathways and 10 hub genes, among which 3 genes (FOS, FOSB, and DUSP1) were identified as potential core genes of clinical significance in AIMAH. In conclusion, the 3 core genes, FOS, FOSB, and DUSP1, identified here might be potential biomarkers for AIMAH, and the current study is of guiding significance for clinical diagnosis and treatment of this disease.

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Background Heat shock proteins (HSPs) are widely involved in tumor occurrence and development and are prognostic markers for multiple tumors. However, the role of HSPs in clear cell renal cell carcinoma (ccRCC) remains unclear. Methods We used Cytoscape to identify hub genes in the ccRCC single-cell sequencing data set from the Gene Expression Omnibus (GEO) data repository. We identified subtypes, C1 and C2, of The Cancer Genome Atlas (TCGA) patients based on the expression of hub genes using unsupervised consensus clustering. Principal component analysis (PCA) was used to verify the clustering differences, and Kaplan–Meier (K-M) estimate was used to verify the survival differences between C1 and C2 patients. We used TIMER 2.0 and CIBERSORT to evaluate the immune cell infiltration of HSP genes and C1 and C2 patients. The R package “pRRophetic” was used to evaluate the sensitivity in C1 and C2 patients to the four first-line treatment drugs. Results We identified six hub genes (HSP90AA1, HSPH1, HSPA1B, HSPA8, and HSPA1A) encoding HSP, five of which were significantly downregulated in TCGA group, and four had a protective effect on prognosis (p <0.05). Survival analysis showed that C1 patients had a better overall survival (p <0.001). TIMER 2.0 analysis showed that three HSP genes were significantly correlated with the infiltration of CD4+ T cells and CD4+ Th1 cells (|cor|>0.5, p<0.001). CIBERSORT showed significant differences in multiple infiltrating immune cells between C1 and C2 patients. Meanwhile, the expression of PD1 was significantly lower in C1 patients than in C2 patients, and the expression of PDL1 is the another way around. Drug sensitivity analysis showed that C1 patients were more sensitive to sorafenib, pazopanib, and axitinib (p <0.001). Conclusion Our research revealed two molecular subtypes of ccRCC based on 6 HSP genes, and revealed significant differences between the two subtypes in terms of clinical prognosis, immune infiltration, and drug sensitivity.

Objective This work focused on investigating the relation of centromeric protein A (CENPA) gene expression with prognosis of papillary renal cell carcinoma (PRCC). Methods We obtained data from PRCC cases in TCGA. Thereafter, CENPA levels between the paired PRCC and matched non-carcinoma samples were analyzed by Wilcoxon rank-sum test, while the relations of clinicopathological characteristics with CENPA level were examined by logistic regression and Wilcoxon rank-sum test. The prognostic value of CENPA was assessed by plotting the receiver operating feature curve (ROC) and calculating the value of area under curve (AUC). In addition, relations between clinicopathological characteristics and PRCC survival were analyzed through Kaplan–Meier (KM) and Cox regression analyses. After dividing the total number of patients into the trial cohort and the validation cohort in a ratio of 7:3, we constructed a nomogram in trial cohort according to multivariate Cox regression results for predicting how CENPA affected patient survival and used the calibration curve to verify its accuracy in both cohorts. We also determined CENPA levels within cancer and matched non-carcinoma samples through immunohistochemistry (IHC). Finally, we utilized functional enrichment for identifying key pathways related to differentially expressed genes (DEGs) between PRCC cases with CENPA up-regulation and down-regulation. Results CENPA expression enhanced in PRCC tissues compared with healthy counterparts (P < 0.001). CENPA up-regulation was related to pathological TNM stage and clinical stage (P < 0.05). Meanwhile, the ROC curves indicated that CENPA had a remarkable diagnostic capacity for PRCC, and the expression of CENPA can significantly improve the predictive accuracy of pathological TNM stage and clinical stage for PRCC. As revealed by KM curves, PRCC cases with CENPA up-regulation were associated with poor survival compared with those with CENPA down-regulation (Risk ratio, RR = 3.07, 95% CI: 1.58–5.97, P = 0.001). In the meantime, univariate as well as multivariate analysis showed an independent association of CENPA with overall survival (OS, P < 0.05) and the nomogram demonstrated superior predictive ability in both cohorts. IHC analysis indicated that PRCC cases showed an increased CENPA positive rate compared with controls. As revealed by functional annotations, CENPA was enriched into pathways associated with neuroactive ligand receptor interactions, cytokine receptor interactions, extracellular matrix regulators, extracellular matrix glycoproteins and nuclear matrisome. Conclusion CENPA expression increases within PRCC samples, which predicts dismal PRCC survival. CENPA may become a molecular prognostic marker and therapeutic target for PRCC patients.

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Objective: This work focused on investigating the relation of centromeric protein A (CENPA) gene expression with prognosis of papillary renal cell carcinoma (PRCC). Methods: We obtained data from PRCC cases in TCGA. Thereafter, CENPA levels between the paired PRCC and matched non-carcinoma samples were analyzed by Wilcoxon rank-and-sum test, while the relations of clinicopathological characteristics with CENPA level were examined by logistic regression and Wilcoxon rank-sum test. The prognostic value of CENPA was assessed by plotting the receiver operating feature curve (ROC) and calculating the value of area under curve (AUC). In addition, relations between clinicopathological features and PRCC survival were analyzed through Kaplan-Meier (KM) and Cox regression analyses. Additionally, we constructed a nomogram according to multivariate Cox regression results for predicting how CENPA affected patient survival. We also determined CENPA levels within cancer and matched non-carcinoma samples through immunohistochemistry (IHC). Finally, we utilized functional enrichment for identifying key pathways related to differentially expressed genes (DEGs) between PRCC cases with CENPA up-regulation and down-regulation. Results: CENPA expression enhanced in PRCC tissues compared with healthy counterparts (P <0.001). CENPA up-regulation was related to clinical stage and pathological TNM stage (P<0.05). Meanwhile, the ROC curve indicated that CENPA had a remarkable diagnostic capacity for PRCC (AUC=0.936). As revealed by KM curve, PRCC cases with CENPA up-regulation were associated with poor survival compared with those with CENPA down-regulation (risk ratio, RR=3.07, 95% CI: 1.58-5.97, P=0.001). In the meantime, univariate as well as multivariate analysis showed an independent association of CENPA with overall survival (OS, P <0.001). IHC analysis indicated that PRCC cases showed an increased CENPA positive rate compared with controls. As revealed by functional annotations, CENPA was enriched into pathways associated with KEGG - NEUROACTIVE - LIGAND - RECEPTOR - INTERACTION, KEGG - CYTOKINE - CYTOKINE - RECEPTOR INTERACTION, NABA - ECM - REGULATORS, NABA - ECM - GLYCOPROTEINS, and NABA - CORE - MATRISOME pathways. Conclusion: CENPA expression increases within PRCC samples, which predicts dismal PRCC survival. CENPA may become a molecular prognostic marker and therapeutic target for PRCC patients.

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