SLC7A11 promotes EMT and metastasis in invasive pituitary neuroendocrine tumors by activating the PI3K/AKT signaling pathway.

Invasive pituitary neuroendocrine tumors (PitNETs) are the most prevalent types of intracranial and neuroendocrine tumors. Due to its aggressive growth and difficulty in complete resection, resulting in a high recurrence rate. The cystine transporter solute carrier family 7 member 11 (SLC7A11) is overexpression in various cancers, which contributes tumor growth, progression, and metastasis by promoting cystine uptake and glutathione biosynthesis. We identified SLC7A11 as an invasive biomarker based on three GEO cohorts. This study aimed to investigate the role of SLC7A11 in invasive PitNETs. Cell proliferation was assessed using CCK-8 and colony formation assays, while cell apoptosis was estimated with flow cytometry. The wound healing assay and transwell assay was utilized to evaluate migration and invasion ability. Our findings demonstrated that SLC7A11 was markedly upregulated in invasive PitNETs, and was associated with the invasiveness of PitNETs. Knockdown of SLC7A11 could largely suppress tumor cell proliferation, migration, and invasion, while inducing apoptosis. Furthermore, SLC7A11 depletion was implicated in regulating EMT and inactivating the PI3K/AKT signaling pathway. These insights suggest SLC7A11 as a potential therapeutic target for invasive PitNETs.

Investigation of cuproptosis regulator-mediated modification patterns and SLC30A7 function in GBM.

BACKGROUND: Copper-dependent controlled cell death (cuproptosis) is a novel cell death modality that is distinct from known cell death mechanisms. Nonetheless, the potential role of the cuproptosis regulator in tumour microenvironment (TME) of GBM remains unknown. METHODS: Based on 13 widely recognised cuproptosis regulators, the cuproptosis regulation patterns and the biological characteristics of each pattern were comprehensively assessed in GBMs. Machine learning strategies were used to construct a CupScore to quantify the cuproptosis regulation patterns of individual tumours. A PPI network was constructed to predict core-associated genes of cuproptosis regulators. The function of the novel cuproptosis regulators SLC30A7 was examined by in vitro and in vivo experiment. RESULTS: We identified three distinct cuproptosis regulation patterns, including immune activation, metabolic activation, and immunometabolic double deletion patterns. The CupScore was shown to predict the abundance of tumour inflammation, molecular subtype, stromal activity, gene variation, signalling pathways, and patient prognosis. The low CupScore subtype was characterised by immune activation, isocitrate dehydrogenase mutations, sensitivity to chemotherapy, and clinical benefits. The high CupScore subtype was characterised by activation of the stroma and metabolism and poor survival. Novel cuproptosis regulator SLC30A7 knockdown inhibited the cuproptosi via JAK2/STAT3/ATP7A pathway in GBM. CONCLUSION: Cuproptosis regulators have been shown to play a vital role in TME complexity. Constructing CupScores were trained to evaluate the regulation patterns of cuproptosis in individual tumours. The novel cuproptosis-related genes SLC30A7 was involved in regulation the tumorigenicity of GBM cell via JAK2/STAT3/ATP7A pathway in vitro and in vivo.

A novel anoikis-related gene signature identifies LYPD1 as a novel therapy target for bladder cancer.

Bladder cancer (BLCA) is a malignant tumor associated with unfavorable outcomes. Studies suggest that anoikis plays a crucial role in tumor progression and cancer cell metastasis. However, its specific role in bladder cancer remains poorly understood. Our objective was to identify anoikis-related genes (ARGs) and subsequently construct a risk model to assess their potential for predicting the prognosis of bladder cancer.The transcriptome data and clinical data of BLCA patients were sourced from The Cancer Genome Atlas and GEO database. We then performed the differential expression analysis to screen differentially expressed ARGs. Subsequently, we conducted non-negative matrix factorization (NMF) clustering analysis to establish molecular subtypes based on the differentially expressed ARGs. The CIBERSORT algorithm was used to estimate the quantification of different cell infiltration in BLCA tumor microenviroment. A prognostic risk model containing 7 ARGs was established using Lasso-Cox regression analysis. The nomogram was built for predicting the survival probability of BLCA patients. To determine the drug sensitivity of each sample from the high- and low-risk groups, the R package "pRRophetic" was performed. Finally, the role of LYPD1 was explored in BLCA cell lines.We identified 90 differential expression ARGs and NMF clustering categorizated the BLCA patientss into two distinct groups (cluster A and B). Patients in cluster A had a better prognosis than those in cluster B. Then, we established a ARGs risk model including CALR, FASN, FOSL1, JUN, LYPD1, MST1R, and SATB1, which was validated in the train and test set. The results suggested overall survival rate was much higher in low risk group than high risk group. The cox regression analysis, ROC curve analysis, and nomogram collectively demonstrated that the risk model served as an independent prognostic factor. The high risk group had a higher level TME scores compared to the low risk group. Furthermore, LYPD1 was low expression in BLCA cells and overexpression of LYPD1 inhibits the prolifearation, migration and invasion.In the current study, we have identified differential expression ARGs and constructed a risk model with the promise for guiding prognostic predictions and provided a therapeutic target for patients with BLCA.

TUBA1C expression promotes proliferation by regulating the cell cycle and indicates poor prognosis in glioma.

Gliomas are the major type of primary brain tumors. Accumulating research has demonstrated that tubulin is connected with the development and malignant progression of tumors. TUBA1C is a subtype of α-tubulin and is linked to prognosis in multiple cancers. In this study, the prognosis-related gene TUBA1C in glioma was identified and analyzed by bioinformatic approaches such as Kaplan-Meier (KM) survival time analysis, univariate and multivariate Cox analysis, receiver operating characteristic (ROC) analysis and functional enrichment analysis. Based on the above analyses, we found that glioma tissues had significantly higher expression of TUBA1C than normal brain tissues, and high expression of TUBA1C has worse prognosis in glioma. Gene set enrichment analysis (GSEA) revealed the signaling pathways related to the cell cycle. Furthermore, knockdown of TUBA1C also inhibited proliferation and migration and caused apoptosis and G2/M phase arrest in glioma cells. This study demonstrated that high TUBA1C expression correlated with poor outcomes in glioma patients and that knocking down TUBA1C suppressed glioma cell proliferation via cell cycle arrest. In addition, TUBA1C might be a therapeutic biomarker for gliomas.