Identification of key pseudogenes in nasopharyngeal carcinoma based on RNA-Seq analysis

Construction and analysis of pseudogene-related ceRNA network in breast cancer

Breast cancer (BC) is one of the leading causes of cancer-related deaths in women. The present study explored the potential role of pseudogenes in BC via construction and analysis of a competing endogenous RNA (ceRNA) network through a three-step process. First, we screened differentially expressed genes in nine BC datasets. Then the gene-pseudogenes pairs (nine hub genes) were selected according to the functional enrichment and correlation analysis. Second, the candidate hub genes and interacting miRNAs were used to construct the ceRNA network. Further analysis of the ceRNA network revealed a crucial ceRNA module with two genes-pseudogene pairs and two miRNAs. The in-depth analysis identified the GBP1/hsa-miR-30d-5p/GBP1P1 axis as a potential tumorigenic axis in BC patients. In the third step, the GBP1/hsa-miR-30d-5p/GBP1P1 axis expression level was assessed in 40 tumor/normal BC patients and MCF-7 cell lines. The expression of GBP1 and GBP1P1 was significantly higher in the tumor compared to the normal tissue. However, the expression of hsa-miR-30d-5p was lower in tumor samples. Then, we introduced the GBP1P1 pseudogene into the MCF-7 cell line to evaluate its effect on GBP1 and hsa-miR-30d-5p expression. As expected, the GBP1 level increased while the hsa-miR-30d-5p level decreased in the GBP1P1-overexprsssing cell line. In addition, the oncogenic properties of MCF-7 (cell viability, clonogenicity, and migration) were improved after GBP1P1 overexpression. In conclusion, we report a ceRNA network that may provide new insight into the role of pseudogenes in BC development.

Molecular Characterization and Prognosis of Lactate-Related Genes in Lung Adenocarcinoma

Objective: To explore the lactate-related genes (LRGs) in lung adenocarcinoma (LUAD) by various methods, construct a prognostic model, and explore the relationship between lactate subtypes and the immune tumor microenvironment (TME). Methods: 24 LRGs were collected. The mutation landscape and the prognosis value of LRGs were explored by using The Cancer Genome Atlas (TCGA) data. Consensus clustering analysis was used for different lactate subtype identification. Based on the lactate subtypes, we explore the landscape of TME cell infiltration. A risk-score was calculated by using the LASSO-Cox analysis. A quantitative real-time PCR assay was utilized to validate the expression of characteristic genes in clinical cancer tissues and paracarinoma tissues from LUAD patients. Results: Comparing the normal samples, 18 LRGs were differentially expressed in tumor samples, which revealed that the differential expression of LRGs may be related to Copy Number Variation (CNV) alterations. The two distinct lactate subtypes were defined. Compared to patients in the LRGcluster A group, LUAD patients in the LRGcluster B group achieved better survival. The prognostic model was constructed based on differentially expressed genes (DEGs) via the LASSO-Cox analysis, which showed the accuracy of predicting the prognosis of LUAD patients using the ROC curve. A high-risk score was related to a high immune score, stromal score, and tumor mutation burden (TMB). Patients had better OS with low risk compared with those with high risk. The sensitivities of different risk groups to chemotherapeutic drugs were explored. Finally, the expression of characteristic genes in clinical cancer tissues and paracarinoma tissues from LUAD patients was verified via qRT-PCR. Conclusions: The lactate subtypes were independent prognostic biomarkers in LUAD. Additionally, the difference in the lactate subtypes was an indispensable feature for the individual TME. The comprehensive evaluation of the lactate subtypes in the single tumor would help us to understand the infiltration characteristics of TME and guide immunotherapy strategies.