The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma

A novel TAp73-inhibitory compound counteracts stemness features of glioblastoma stem cells

Glioblastoma (GB) is the most common and fatal type of primary malignant brain tumor for which effective therapeutics are still lacking. GB stem cells, with tumor-initiating and self-renewal capacity, are mostly responsible for GB malignancy, representing a crucial target for therapies. The TP73 gene, which is highly expressed in GB, gives rise to the TAp73 isoform, a pleiotropic protein that regulates neural stem cell biology; however, its role in cancer has been highly controversial. We inactivated TP73 in human GB stem cells and revealed that TAp73 is required for their stemness potential, acting as a regulator of the transcriptional stemness signatures, highlighting TAp73 as a possible therapeutic target. As proof of concept, we identified a novel natural compound with TAp73-inhibitory capacity, which was highly effective against GB stem cells. The treatment reduced GB stem cell-invasion capacity and stem features, at least in part by TAp73 repression. Our data are consistent with a novel paradigm in which hijacking of p73-regulated neurodevelopmental programs, including neural stemness, might sustain tumor progression, pointing out TAp73 as a therapeutic strategy for GB.

Analysis of ABCC3 in glioma progression: implications for prognosis, immunotherapy, and drug resistance

As a primary brain cancer, glioma presents significant challenges in treatment and prognosis. Identifying reliable biomarkers is crucial for improving patient outcomes. This study focuses on the ABCC3 gene, exploring its function as a standalone predictive indictor and its correlation with immune infiltration and resistance to chemotherapy in glioma. A multi-faceted approach was adopted for this analysis. We scrutinized the RNA expression patterns of the ABCC3 gene across a spectrum of cancer types, with a concentrated focus on glioma. Our methodological arsenal included bioinformatics analysis, immunohistochemistry (ICH), western blot (WB), and cell counting Kit-8 (CCK8) assays. These techniques were instrumental in gauging the prognostic impact of ABCC3 and elucidating its associations with immune cell infiltration and chemotherapy resistance. The investigation revealed a significant elevated levels of ABCC3 in high grade glioma (HGG) tissues compared to lower grade glioma (LGG) tissues. Notably, upregulation of ABCC3 were associated with a shorter overall survival in patients with glioma. Furthermore, ABCC3 emerged as an independent factor in prognostication, with predictive capability for 1-, 3-, and 5-year survival rates. As far as immune response is concerned, ABCC3's expression correlates positively with the expression of several immune cells and checkpoint genes. The study also uncovered the role of ABCC3 in drug resistance, particularly regarding temozolomide (TMZ), a primary therapeutic agent in glioma treatment. The study reveals ABCC3 as a significant biomarker in glioma, associated with lower survival, enhanced immune infiltration, and increased resistance to chemotherapy. These findings emphasize its promise as a novel target for glioma therapies.

Stemness-related lncRNAs signature as a biologic prognostic model for head and neck squamous cell carcinoma

Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are particularly important for tumor cell growth and migration, and recurrence and drug resistance, including head and neck squamous cell carcinoma (HNSCC). The purpose of this study was to explore stemness-related lncRNAs (SRlncRNAs) that could be used for prognosis of patients with HNSCC. HNSCC RNA sequencing data and matched clinical data were obtained from TCGA database, and stem cell characteristic genes related to HNSCC mRNAsi were obtained from the online database by WGCNA analysis, respectively. Further, SRlncRNAs were obtained. Then, the prognostic model was constructed to forecast patient survival through univariate Cox regression and LASSO-Cox method based on SRlncRNAs. Kaplan-Meier, ROC and AUC were used to evaluate the predictive ability of the model. Moreover, we probed the underlying biological functions, signalling pathways and immune status hidden within differences in prognosis of patients. We explored whether the model could guide personalized treatments included immunotherapy and chemotherapy for HNSCC patients. At last, RT-qPCR was performed to analyze the expressions levels of SRlncRNAs in HNSCC cell lines. A SRlncRNAs signature was identified based on 5 SRlncRNAs (AC004943.2, AL022328.1, MIR9-3HG, AC015878.1 and FOXD2-AS1) in HNSCC. Also, risk scores were correlated with the abundance of tumor-infiltrating immune cells, whereas HNSCC-nominated chemotherapy drugs were considerably different from one another. The final finding was that these SRlncRNAs were abnormally expressed in HNSCCCS according to the results of RT-qPCR. These 5 SRlncRNAs signature, as a potential prognostic biomarker, can be utilized for personalized medicine in HNSCC patients.

Development and Optimization of a Prognostic Model Associated with Stemness Genes in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, which is associated with a variety of risk factors. Cancer stem cells are self-renewal cells, which can promote the occurrence and metastasis of tumors and enhance the drug resistance of tumor treatment. This study aimed to develop a stemness score model to assess the prognosis of hepatocellular carcinoma (HCC) patients for the optimization of treatment. The single-cell sequencing data GSE149614 was downloaded from the GEO database. Then, we compared the gene expression of hepatic stem cells and other hepatocytes in tumor samples to screen differentially expressed genes related to stemness. R package "clusterProfiler" was used to explore the potential function of stemness-related genes. We then constructed a prognostic model using LASSO regression analysis based on the TCGA and GSE14520 cohorts. The associations of stemness score with clinical features, drug sensitivity, gene mutation, and tumor immune microenvironment were further explored. R package "rms" was used to construct the nomogram model. A total of 18 stemness-related genes were enrolled to construct the prognosis model. Kaplan-Meier analysis proved the good performance of the stemness score model at predicting overall survival (OS) of HCC patients. The stemness score was closely associated with clinical features, drug sensitivity, and tumor immune microenvironment of HCC. The infiltration level of CD8+ T cells was lower, and tumor-associated macrophages were higher in patients with high-stemness score, indicating an immunosuppressive microenvironment. Our study established an 18 stemness-related gene model that reliably predicts OS in HCC. The findings may help clarify the biological characteristics and progression of HCC and help the future diagnosis and therapy of HCC.