DNA repair/recombination protein 54L promotes the progression of lung adenocarcinoma by activating mTORC1 pathway

Pleiotrophin Activates cMet- and mTORC1-Dependent Protein Synthesis through PTPRZ1-The Role of ανβ3 Integrin

Pleiotrophin (PTN) is a secreted factor that regulates endothelial cell migration through protein tyrosine phosphatase receptor zeta 1 (PTPRZ1) and αvβ3 integrin. Genetic deletion of Ptprz1 results in enhanced endothelial cell proliferation and migration, due to the decreased expression of β3 integrin and the subsequent, enhanced cMet phosphorylation. In the present study, we investigated the effect of PTN and PTPRZ1 on activating the mTORC1 kinase and protein synthesis and identified part of the implicated signaling pathway in endothelial cells. PTN or genetic deletion of Ptprz1 activates protein synthesis in a mTORC1-dependent manner, as shown by the enhanced phosphorylation of the mTORC1-downstream targets ribosomal protein S6 kinase 1 (SK61) and 4E-binding protein 1 (4EBP1) and the upregulation of HIF-1α. The cMet tyrosine kinase inhibitor crizotinib abolishes the stimulatory effects of PTN or PTPRZ1 deletion on mTORC1 activation and protein synthesis, suggesting that mTORC1 activation is downstream of cMet. The mTORC1 inhibitor rapamycin abolishes the stimulatory effect of PTN or PTPRZ1 deletion on endothelial cell migration, suggesting that mTORC1 is involved in the PTN/PTPRZ1-dependent cell migration. The αvβ3 integrin blocking antibody LM609 and the peptide PTN112-136, both known to bind to ανβ3 and inhibit PTN-induced endothelial cell migration, increase cMet phosphorylation and activate mTORC1, suggesting that cMet and mTORC1 activation are required but are not sufficient to stimulate cell migration. Overall, our data highlight novel aspects of the signaling pathway downstream of the PTN/PTPRZ1 axis that regulates endothelial cell functions.

Characterization of a G2M checkpoint-related gene model and subtypes associated with immunotherapy response for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) presents challenges in early diagnosis and effective treatment. In this study, we aimed to establish a prognostic model based on G2M checkpoint-related genes and identify associated clusters in ccRCC through clinical bioinformatic analysis and experimental validation. Utilizing a single-cell RNA dataset (GSE159115) and bulk-sequencing data from The Cancer Genome Atlas (TCGA) database, we analyzed the G2M checkpoint pathway in ccRCC. Differential expression analysis identified 45 genes associated with the G2M checkpoint, leading to the construction of a predictive model with four key genes (E2F2, GTSE1, RAD54L, and UBE2C). The model demonstrated reliable predictive ability for 1-, 3-, and 5-year overall survival, with AUC values of 0.794, 0.790, and 0.794, respectively. Patients in the high-risk group exhibited a worse prognosis, accompanied by significant differences in immune cell infiltration, immune function, TIDE and IPS scores, and drug sensitivities. Two clusters of ccRCC were identified using the "ConsensusClusterPlus" package, cluster 1 exhibited a worse survival rate and was resistant to chemotherapeutic drugs of Axitinib, Erlotinib, Pazopanib, Sunitinib, and Temsirolimus, but not Sorafenib. Targeted experiments on RAD54L, a gene involved in DNA repair processes, revealed its crucial role in inhibiting proliferation, invasion, and migration in 786-O cells. In conclusion, our study offers valuable insights into the molecular mechanisms underlying ccRCC, identifying potential prognostic genes and molecular subtypes associated with the G2M checkpoint. These findings hold promise for guiding personalized treatment strategies in the management of ccRCC.

Identification of hypoxia-related gene signatures based on multi-omics analysis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer and one of the malignancies with the highest incidence rate and mortality worldwide. Hypoxia is a typical feature of tumour microenvironment (TME), which affects the progression of LUAD from multiple molecular levels. However, the underlying molecular mechanisms behind LUAD hypoxia are not fully understood. In this study, we estimated the level of hypoxia by calculating a score based on 15 hypoxia genes. The hypoxia scores were relatively high in LUAD patients with poor prognosis and were bound up with tumour node metastasis (TNM) stage, tumour size, lymph node, age and gender. By comparison of high hypoxia score group and low hypoxia score group, 1820 differentially expressed genes were identified, among which up-regulated genes were mainly about cell division and proliferation while down-regulated genes were primarily involved in cilium-related biological processes. Besides, LUAD patients with high hypoxia scores had higher frequencies of gene mutations, among which TP53, TTN and MUC16 had the highest mutation rates. As for DNA methylation, 1015 differentially methylated probes-related genes were found and may play potential roles in tumour-related neurobiological processes and cell signal transduction. Finally, a prognostic model with 25 multi-omics features was constructed and showed good predictive performance. The area under curve (AUC) values of 1-, 3- and 5-year survival reached 0.863, 0.826 and 0.846, respectively. Above all, our findings are helpful in understanding the impact and molecular mechanisms of hypoxia in LUAD.